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rfraposa
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@ -1,7 +0,0 @@
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position: 1
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label: 'Example Datasets'
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collapsible: true
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collapsed: true
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link:
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type: doc
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id: en/getting-started/example-datasets/
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@ -6,7 +6,7 @@ keywords: [clickhouse, network, interfaces, http, tcp, grpc, command-line, clien
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description: ClickHouse provides three network interfaces
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---
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# Interfaces
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# Drivers and Interfaces
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ClickHouse provides three network interfaces (they can be optionally wrapped in TLS for additional security):
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@ -1,7 +1,7 @@
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---
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slug: /en/operations/utilities/
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sidebar_position: 56
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sidebar_label: Utilities
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sidebar_label: List of tools and utilities
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pagination_next: 'en/operations/utilities/clickhouse-copier'
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---
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@ -1,13 +1,33 @@
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---
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slug: /en/sql-reference/data-types/
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sidebar_label: Data Types
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sidebar_label: List of data types
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sidebar_position: 37
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---
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# Data Types
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# ClickHouse Data Types
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ClickHouse can store various kinds of data in table cells.
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ClickHouse can store various kinds of data in table cells. This section describes the supported data types and special considerations for using and/or implementing them if any.
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This section describes the supported data types and special considerations for using and/or implementing them if any.
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:::note
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You can check whether a data type name is case-sensitive in the [system.data_type_families](../../operations/system-tables/data_type_families.md#system_tables-data_type_families) table.
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:::
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You can check whether data type name is case-sensitive in the [system.data_type_families](../../operations/system-tables/data_type_families.md#system_tables-data_type_families) table.
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ClickHouse data types include:
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- **Integer types**: [signed and unsigned integers](./int-uint.md) (`UInt8`, `UInt16`, `UInt32`, `UInt64`, `UInt128`, `UInt256`, `Int8`, `Int16`, `Int32`, `Int64`, `Int128`, `Int256`)
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- **Floating-point numbers**: [floats](./float.md)(`Float32` and `Float64`) and [`Decimal` values](./decimal.md)
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- **Boolean**: ClickHouse has a [`Boolean` type](./boolean.md)
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- **Strings**: [`String`](./string.md) and [`FixedString`](./fixedstring.md)
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- **Dates**: use [`Date`](./date.md) and [`Date32`](./date32.md) for days, and [`DateTime`](./datetime.md) and [`DateTime64`](./datetime64.md) for instances in time
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- **JSON**: the [`JSON` object](./json.md) stores a JSON document in a single column
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- **UUID**: a performant option for storing [`UUID` values](./uuid.md)
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- **Low cardinality types**: use an [`Enum`](./enum.md) when you have a handful of unique values, or use [`LowCardinality`](./lowcardinality.md) when you have up to 10,000 unique values of a column
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- **Arrays**: any column can be defined as an [`Array` of values](./array.md)
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- **Maps**: use [`Map`](./map.md) for storing key/value pairs
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- **Aggregation function types**: use [`SimpleAggregateFunction`](./simpleaggregatefunction.md) and [`AggregateFunction`](./aggregatefunction.md) for storing the intermediate status of aggregate function results
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- **Nested data structures**: A [`Nested` data structure](./nested-data-structures/index.md) is like a table inside a cell
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- **Tuples**: A [`Tuple` of elements](./tuple.md), each having an individual type.
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- **Nullable**: [`Nullbale`](./nullable.md) allows you to store a value as `NULL` when a value is "missing" (instead of the column gettings its default value for the data type)
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- **IP addresses**: use [`IPv4`](./domains/ipv4.md) and [`IPv6`](./domains/ipv6.md) to efficiently store IP addresses
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- **Geo types**: for[ geographical data](./geo.md), including `Point`, `Ring`, `Polygon` and `MultiPolygon`
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- **Special data types**: including [`Expression`](./special-data-types/expression.md), [`Set`](./special-data-types/set.md), [`Nothing`](./special-data-types/nothing.md) and [`Interval`](./special-data-types/interval.md)
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@ -1,7 +1,105 @@
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---
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slug: /en/sql-reference/data-types/nested-data-structures/
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sidebar_label: Nested Data Structures
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sidebar_position: 54
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slug: /en/sql-reference/data-types/nested-data-structures/nested
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sidebar_position: 57
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sidebar_label: Nested(Name1 Type1, Name2 Type2, ...)
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---
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# Nested Data Structures
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# Nested
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## Nested(name1 Type1, Name2 Type2, …)
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A nested data structure is like a table inside a cell. The parameters of a nested data structure – the column names and types – are specified the same way as in a [CREATE TABLE](../../../sql-reference/statements/create/table.md) query. Each table row can correspond to any number of rows in a nested data structure.
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Example:
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``` sql
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CREATE TABLE test.visits
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(
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CounterID UInt32,
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StartDate Date,
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Sign Int8,
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IsNew UInt8,
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VisitID UInt64,
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UserID UInt64,
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...
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Goals Nested
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(
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ID UInt32,
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Serial UInt32,
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EventTime DateTime,
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Price Int64,
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OrderID String,
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CurrencyID UInt32
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),
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...
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) ENGINE = CollapsingMergeTree(StartDate, intHash32(UserID), (CounterID, StartDate, intHash32(UserID), VisitID), 8192, Sign)
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```
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This example declares the `Goals` nested data structure, which contains data about conversions (goals reached). Each row in the ‘visits’ table can correspond to zero or any number of conversions.
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When [flatten_nested](../../../operations/settings/settings.md#flatten-nested) is set to `0` (which is not by default), arbitrary levels of nesting are supported.
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In most cases, when working with a nested data structure, its columns are specified with column names separated by a dot. These columns make up an array of matching types. All the column arrays of a single nested data structure have the same length.
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Example:
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``` sql
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SELECT
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Goals.ID,
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Goals.EventTime
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FROM test.visits
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WHERE CounterID = 101500 AND length(Goals.ID) < 5
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LIMIT 10
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```
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``` text
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┌─Goals.ID───────────────────────┬─Goals.EventTime───────────────────────────────────────────────────────────────────────────┐
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│ [1073752,591325,591325] │ ['2014-03-17 16:38:10','2014-03-17 16:38:48','2014-03-17 16:42:27'] │
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│ [1073752] │ ['2014-03-17 00:28:25'] │
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│ [1073752] │ ['2014-03-17 10:46:20'] │
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│ [1073752,591325,591325,591325] │ ['2014-03-17 13:59:20','2014-03-17 22:17:55','2014-03-17 22:18:07','2014-03-17 22:18:51'] │
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│ [] │ [] │
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│ [1073752,591325,591325] │ ['2014-03-17 11:37:06','2014-03-17 14:07:47','2014-03-17 14:36:21'] │
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│ [] │ [] │
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│ [] │ [] │
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│ [591325,1073752] │ ['2014-03-17 00:46:05','2014-03-17 00:46:05'] │
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│ [1073752,591325,591325,591325] │ ['2014-03-17 13:28:33','2014-03-17 13:30:26','2014-03-17 18:51:21','2014-03-17 18:51:45'] │
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└────────────────────────────────┴───────────────────────────────────────────────────────────────────────────────────────────┘
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```
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It is easiest to think of a nested data structure as a set of multiple column arrays of the same length.
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The only place where a SELECT query can specify the name of an entire nested data structure instead of individual columns is the ARRAY JOIN clause. For more information, see “ARRAY JOIN clause”. Example:
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``` sql
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SELECT
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Goal.ID,
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Goal.EventTime
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FROM test.visits
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ARRAY JOIN Goals AS Goal
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WHERE CounterID = 101500 AND length(Goals.ID) < 5
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LIMIT 10
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```
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``` text
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┌─Goal.ID─┬──────Goal.EventTime─┐
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│ 1073752 │ 2014-03-17 16:38:10 │
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│ 591325 │ 2014-03-17 16:38:48 │
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│ 591325 │ 2014-03-17 16:42:27 │
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│ 1073752 │ 2014-03-17 00:28:25 │
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│ 1073752 │ 2014-03-17 10:46:20 │
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│ 1073752 │ 2014-03-17 13:59:20 │
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│ 591325 │ 2014-03-17 22:17:55 │
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│ 591325 │ 2014-03-17 22:18:07 │
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│ 591325 │ 2014-03-17 22:18:51 │
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│ 1073752 │ 2014-03-17 11:37:06 │
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└─────────┴─────────────────────┘
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```
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You can’t perform SELECT for an entire nested data structure. You can only explicitly list individual columns that are part of it.
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For an INSERT query, you should pass all the component column arrays of a nested data structure separately (as if they were individual column arrays). During insertion, the system checks that they have the same length.
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For a DESCRIBE query, the columns in a nested data structure are listed separately in the same way.
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The ALTER query for elements in a nested data structure has limitations.
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@ -1,105 +0,0 @@
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---
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slug: /en/sql-reference/data-types/nested-data-structures/nested
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sidebar_position: 57
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sidebar_label: Nested(Name1 Type1, Name2 Type2, ...)
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---
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# Nested
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## Nested(name1 Type1, Name2 Type2, …)
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A nested data structure is like a table inside a cell. The parameters of a nested data structure – the column names and types – are specified the same way as in a [CREATE TABLE](../../../sql-reference/statements/create/table.md) query. Each table row can correspond to any number of rows in a nested data structure.
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Example:
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``` sql
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CREATE TABLE test.visits
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(
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CounterID UInt32,
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StartDate Date,
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Sign Int8,
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IsNew UInt8,
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VisitID UInt64,
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UserID UInt64,
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...
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Goals Nested
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(
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ID UInt32,
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Serial UInt32,
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EventTime DateTime,
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Price Int64,
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OrderID String,
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CurrencyID UInt32
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),
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...
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) ENGINE = CollapsingMergeTree(StartDate, intHash32(UserID), (CounterID, StartDate, intHash32(UserID), VisitID), 8192, Sign)
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```
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This example declares the `Goals` nested data structure, which contains data about conversions (goals reached). Each row in the ‘visits’ table can correspond to zero or any number of conversions.
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When [flatten_nested](../../../operations/settings/settings.md#flatten-nested) is set to `0` (which is not by default), arbitrary levels of nesting are supported.
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In most cases, when working with a nested data structure, its columns are specified with column names separated by a dot. These columns make up an array of matching types. All the column arrays of a single nested data structure have the same length.
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Example:
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``` sql
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SELECT
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Goals.ID,
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Goals.EventTime
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FROM test.visits
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WHERE CounterID = 101500 AND length(Goals.ID) < 5
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LIMIT 10
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```
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``` text
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┌─Goals.ID───────────────────────┬─Goals.EventTime───────────────────────────────────────────────────────────────────────────┐
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│ [1073752,591325,591325] │ ['2014-03-17 16:38:10','2014-03-17 16:38:48','2014-03-17 16:42:27'] │
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│ [1073752] │ ['2014-03-17 00:28:25'] │
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│ [1073752] │ ['2014-03-17 10:46:20'] │
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│ [1073752,591325,591325,591325] │ ['2014-03-17 13:59:20','2014-03-17 22:17:55','2014-03-17 22:18:07','2014-03-17 22:18:51'] │
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│ [] │ [] │
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│ [1073752,591325,591325] │ ['2014-03-17 11:37:06','2014-03-17 14:07:47','2014-03-17 14:36:21'] │
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│ [] │ [] │
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│ [] │ [] │
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│ [591325,1073752] │ ['2014-03-17 00:46:05','2014-03-17 00:46:05'] │
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│ [1073752,591325,591325,591325] │ ['2014-03-17 13:28:33','2014-03-17 13:30:26','2014-03-17 18:51:21','2014-03-17 18:51:45'] │
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└────────────────────────────────┴───────────────────────────────────────────────────────────────────────────────────────────┘
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```
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It is easiest to think of a nested data structure as a set of multiple column arrays of the same length.
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The only place where a SELECT query can specify the name of an entire nested data structure instead of individual columns is the ARRAY JOIN clause. For more information, see “ARRAY JOIN clause”. Example:
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``` sql
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SELECT
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Goal.ID,
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Goal.EventTime
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FROM test.visits
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ARRAY JOIN Goals AS Goal
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WHERE CounterID = 101500 AND length(Goals.ID) < 5
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LIMIT 10
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```
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``` text
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┌─Goal.ID─┬──────Goal.EventTime─┐
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│ 1073752 │ 2014-03-17 16:38:10 │
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│ 591325 │ 2014-03-17 16:38:48 │
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│ 591325 │ 2014-03-17 16:42:27 │
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│ 1073752 │ 2014-03-17 00:28:25 │
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│ 1073752 │ 2014-03-17 10:46:20 │
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│ 1073752 │ 2014-03-17 13:59:20 │
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│ 591325 │ 2014-03-17 22:17:55 │
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│ 591325 │ 2014-03-17 22:18:07 │
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│ 591325 │ 2014-03-17 22:18:51 │
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│ 1073752 │ 2014-03-17 11:37:06 │
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└─────────┴─────────────────────┘
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```
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You can’t perform SELECT for an entire nested data structure. You can only explicitly list individual columns that are part of it.
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For an INSERT query, you should pass all the component column arrays of a nested data structure separately (as if they were individual column arrays). During insertion, the system checks that they have the same length.
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For a DESCRIBE query, the columns in a nested data structure are listed separately in the same way.
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The ALTER query for elements in a nested data structure has limitations.
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@ -1,7 +1,7 @@
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---
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slug: /en/sql-reference/statements/
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sidebar_position: 1
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sidebar_label: Statements
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sidebar_label: List of statements
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---
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# ClickHouse SQL Statements
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@ -1,5 +1,5 @@
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---
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slug: /zh/faq/general
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slug: /zh/faq/general/overview
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---
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# 常见问题 {#chang-jian-wen-ti}
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@ -1,10 +0,0 @@
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---
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slug: /zh/sql-reference/functions/geo/
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sidebar_label: Geo
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sidebar_position: 62
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title: "Geo Functions"
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---
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import Content from '@site/docs/en/sql-reference/functions/geo/index.md';
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<Content />
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@ -1,5 +1,5 @@
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---
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slug: /zh/sql-reference/statements/alter/
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slug: /zh/sql-reference/statements/alter/overview
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sidebar_position: 35
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sidebar_label: ALTER
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---
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@ -1,11 +0,0 @@
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---
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slug: /zh/sql-reference/statements/create/
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sidebar_label: CREATE
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sidebar_position: 34
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---
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# CREATE语法 {#create-queries}
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CREATE语法包含以下子集:
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- [DATABASE](../../../sql-reference/statements/create/database.md)
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