Fixed broken links

docs/en/development/continuous-integration.md

@@ -151,7 +151,7 @@ checks page](../development/build.md#you-dont-have-to-build-clickhouse), or buil
 
 
 ## Functional Stateful Tests
-Runs [stateful functional tests](tests.md#functional-tests). Treat them in the same way as the functional stateless tests. The difference is that they require `hits` and `visits` tables from the [Yandex.Metrica dataset](../getting-started/example-datasets/metrica.md) to run.
+Runs [stateful functional tests](tests.md#functional-tests). Treat them in the same way as the functional stateless tests. The difference is that they require `hits` and `visits` tables from the [clickstream dataset](../example-datasets/metrica.md) to run.
 
 
 ## Integration Tests

docs/en/engines/table-engines/integrations/kafka.md

@@ -134,7 +134,7 @@ Example:
 
   SELECT level, sum(total) FROM daily GROUP BY level;
 ```
-To improve performance, received messages are grouped into blocks the size of [max_insert_block_size](../../../operations/settings/settings/#settings-max_insert_block_size). If the block wasn’t formed within [stream_flush_interval_ms](../../../operations/settings/settings/#stream-flush-interval-ms) milliseconds, the data will be flushed to the table regardless of the completeness of the block.
+To improve performance, received messages are grouped into blocks the size of [max_insert_block_size](../../../operations/settings/settings.md#settings-max_insert_block_size). If the block wasn’t formed within [stream_flush_interval_ms](../../../operations/settings/settings.md/#stream-flush-interval-ms) milliseconds, the data will be flushed to the table regardless of the completeness of the block.
 
 To stop receiving topic data or to change the conversion logic, detach the materialized view:
 

docs/en/example-datasets/cell-towers.md

@@ -95,7 +95,7 @@ SELECT mcc, count() FROM cell_towers GROUP BY mcc ORDER BY count() DESC LIMIT 10
 
 So, the top countries are: the USA, Germany, and Russia.
 
-You may want to create an [External Dictionary](../../sql-reference/dictionaries/external-dictionaries/external-dicts.md) in ClickHouse to decode these values.
+You may want to create an [External Dictionary](../sql-reference/dictionaries/external-dictionaries/external-dicts.md) in ClickHouse to decode these values.
 
 
 ## Use case {#use-case}

docs/en/example-datasets/menus.md

@@ -39,7 +39,7 @@ The data is normalized consisted of four tables:
 
 ## Create the Tables {#create-tables}
 
-We use [Decimal](../../sql-reference/data-types/decimal.md) data type to store prices.
+We use [Decimal](../sql-reference/data-types/decimal.md) data type to store prices.
 
 ```sql
 CREATE TABLE dish
@@ -115,17 +115,17 @@ clickhouse-client --format_csv_allow_single_quotes 0 --input_format_null_as_defa
 clickhouse-client --format_csv_allow_single_quotes 0 --input_format_null_as_default 0 --date_time_input_format best_effort --query "INSERT INTO menu_item FORMAT CSVWithNames" < MenuItem.csv
 ```
 
-We use [CSVWithNames](../../interfaces/formats.md#csvwithnames) format as the data is represented by CSV with header.
+We use [CSVWithNames](../interfaces/formats.md#csvwithnames) format as the data is represented by CSV with header.
 
 We disable `format_csv_allow_single_quotes` as only double quotes are used for data fields and single quotes can be inside the values and should not confuse the CSV parser.
 
-We disable [input_format_null_as_default](../../operations/settings/settings.md#settings-input-format-null-as-default) as our data does not have [NULL](../../sql-reference/syntax.md#null-literal). Otherwise ClickHouse will try to parse `\N` sequences and can be confused with `\` in data.
+We disable [input_format_null_as_default](../operations/settings/settings.md#settings-input-format-null-as-default) as our data does not have [NULL](../sql-reference/syntax.md#null-literal). Otherwise ClickHouse will try to parse `\N` sequences and can be confused with `\` in data.
 
-The setting [date_time_input_format best_effort](../../operations/settings/settings.md#settings-date_time_input_format) allows to parse [DateTime](../../sql-reference/data-types/datetime.md)  fields in wide variety of formats. For example, ISO-8601 without seconds like '2000-01-01 01:02' will be recognized. Without this setting only fixed DateTime format is allowed.
+The setting [date_time_input_format best_effort](../operations/settings/settings.md#settings-date_time_input_format) allows to parse [DateTime](../sql-reference/data-types/datetime.md)  fields in wide variety of formats. For example, ISO-8601 without seconds like '2000-01-01 01:02' will be recognized. Without this setting only fixed DateTime format is allowed.
 
 ## Denormalize the Data {#denormalize-data}
 
-Data is presented in multiple tables in [normalized form](https://en.wikipedia.org/wiki/Database_normalization#Normal_forms). It means you have to perform [JOIN](../../sql-reference/statements/select/join.md#select-join) if you want to query, e.g. dish names from menu items.
+Data is presented in multiple tables in [normalized form](https://en.wikipedia.org/wiki/Database_normalization#Normal_forms). It means you have to perform [JOIN](../sql-reference/statements/select/join.md#select-join) if you want to query, e.g. dish names from menu items.
 For typical analytical tasks it is way more efficient to deal with pre-JOINed data to avoid doing `JOIN` every time. It is called "denormalized" data.
 
 We will create a table `menu_item_denorm` where will contain all the data JOINed together:

docs/en/example-datasets/metrica.md

@@ -73,6 +73,6 @@ clickhouse-client --query "SELECT COUNT(*) FROM datasets.visits_v1"
 
 ## Example Queries {#example-queries}
 
-[The ClickHouse tutorial](../../getting-started/tutorial.md) is based on this web analytics dataset, and the recommended way to get started with this dataset is to go through the tutorial.
+[The ClickHouse tutorial](../../tutorial.md) is based on this web analytics dataset, and the recommended way to get started with this dataset is to go through the tutorial.
 
 Additional examples of queries to these tables can be found among [stateful tests](https://github.com/ClickHouse/ClickHouse/tree/master/tests/queries/1_stateful) of ClickHouse (they are named `test.hits` and `test.visits` there).

docs/en/example-datasets/opensky.md

@@ -60,9 +60,9 @@ ls -1 flightlist_*.csv.gz | xargs -P100 -I{} bash -c 'gzip -c -d "{}" | clickhou
 `xargs -P100` specifies to use up to 100 parallel workers but as we only have 30 files, the number of workers will be only 30.
 - For every file, `xargs` will run a script with `bash -c`. The script has substitution in form of `{}` and the `xargs` command will substitute the filename to it (we have asked it for `xargs` with `-I{}`).
 - The script will decompress the file (`gzip -c -d "{}"`) to standard output (`-c` parameter) and the output is redirected to `clickhouse-client`.
-- We also asked to parse [DateTime](../../sql-reference/data-types/datetime.md) fields with extended parser ([--date_time_input_format best_effort](../../operations/settings/settings.md#settings-date_time_input_format)) to recognize ISO-8601 format with timezone offsets.
+- We also asked to parse [DateTime](../sql-reference/data-types/datetime.md) fields with extended parser ([--date_time_input_format best_effort](../operations/settings/settings.md#settings-date_time_input_format)) to recognize ISO-8601 format with timezone offsets.
 
-Finally, `clickhouse-client` will do insertion. It will read input data in [CSVWithNames](../../interfaces/formats.md#csvwithnames) format. 
+Finally, `clickhouse-client` will do insertion. It will read input data in [CSVWithNames](../interfaces/formats.md#csvwithnames) format. 
 
 Parallel upload takes 24 seconds.
 

docs/en/example-datasets/recipes.md

@@ -50,13 +50,13 @@ clickhouse-client --query "
 This is a showcase how to parse custom CSV, as it requires multiple tunes.
 
 Explanation:
--   The dataset is in CSV format, but it requires some preprocessing on insertion; we use table function [input](../../sql-reference/table-functions/input.md) to perform preprocessing;
+-   The dataset is in CSV format, but it requires some preprocessing on insertion; we use table function [input](../sql-reference/table-functions/input.md) to perform preprocessing;
 -   The structure of CSV file is specified in the argument of the table function `input`;
 -   The field `num` (row number) is unneeded - we parse it from file and ignore;
 -   We use `FORMAT CSVWithNames` but the header in CSV will be ignored (by command line parameter `--input_format_with_names_use_header 0`), because the header does not contain the name for the first field;
 -   File is using only double quotes to enclose CSV strings; some strings are not enclosed in double quotes, and single quote must not be parsed as the string enclosing - that's why we also add the `--format_csv_allow_single_quote 0` parameter;
 -   Some strings from CSV cannot parse, because they contain `\M/` sequence at the beginning of the value; the only value starting with backslash in CSV can be `\N` that is parsed as SQL NULL. We add `--input_format_allow_errors_num 10` parameter and up to ten malformed records can be skipped;
--   There are arrays for ingredients, directions and NER fields; these arrays are represented in unusual form: they are serialized into string as JSON and then placed in CSV - we parse them as String and then use [JSONExtract](../../sql-reference/functions/json-functions/) function to transform it to Array.
+-   There are arrays for ingredients, directions and NER fields; these arrays are represented in unusual form: they are serialized into string as JSON and then placed in CSV - we parse them as String and then use [JSONExtract](../sql-reference/functions/json-functions/) function to transform it to Array.
 
 ## Validate the Inserted Data
 
@@ -80,7 +80,7 @@ Result:
 
 ### Top Components by the Number of Recipes:
 
-In this example we learn how to use [arrayJoin](../../sql-reference/functions/array-join/) function to expand an array into a set of rows.
+In this example we learn how to use [arrayJoin](../sql-reference/functions/array-join/) function to expand an array into a set of rows.
 
 Query:
 
@@ -185,7 +185,7 @@ Result:
 10 rows in set. Elapsed: 0.215 sec. Processed 2.23 million rows, 1.48 GB (10.35 million rows/s., 6.86 GB/s.)
 ```
 
-In this example, we involve [has](../../sql-reference/functions/array-functions/#hasarr-elem) function to filter by array elements and sort by the number of directions.
+In this example, we involve [has](../sql-reference/functions/array-functions/#hasarr-elem) function to filter by array elements and sort by the number of directions.
 
 There is a wedding cake that requires the whole 126 steps to produce! Show that directions:
 

docs/en/example-datasets/uk-price-paid.md

@@ -54,9 +54,9 @@ In this example, we define the structure of source data from the CSV file and sp
 The preprocessing is:
 - splitting the postcode to two different columns `postcode1` and `postcode2` that is better for storage and queries;
 - coverting the `time` field to date as it only contains 00:00 time;
-- ignoring the [UUid](../../sql-reference/data-types/uuid.md) field because we don't need it for analysis;
-- transforming `type` and `duration` to more readable Enum fields with function [transform](../../sql-reference/functions/other-functions.md#transform);
-- transforming `is_new` and `category` fields from single-character string (`Y`/`N` and `A`/`B`) to [UInt8](../../sql-reference/data-types/int-uint.md#uint8-uint16-uint32-uint64-uint256-int8-int16-int32-int64-int128-int256) field with 0 and 1.
+- ignoring the [UUid](../sql-reference/data-types/uuid.md) field because we don't need it for analysis;
+- transforming `type` and `duration` to more readable Enum fields with function [transform](../sql-reference/functions/other-functions.md#transform);
+- transforming `is_new` and `category` fields from single-character string (`Y`/`N` and `A`/`B`) to [UInt8](../sql-reference/data-types/int-uint.md#uint8-uint16-uint32-uint64-uint256-int8-int16-int32-int64-int128-int256) field with 0 and 1.
 
 Preprocessed data is piped directly to `clickhouse-client` to be inserted into ClickHouse table in streaming fashion.
 
@@ -352,7 +352,7 @@ Result:
 
 ## Let's Speed Up Queries Using Projections {#speedup-with-projections}
 
-[Projections](../../sql-reference/statements/alter/projection.md) allow to improve queries speed by storing pre-aggregated data.
+[Projections](../sql-reference/statements/alter/projection.md) allow to improve queries speed by storing pre-aggregated data.
 
 ### Build a Projection {#build-projection}
 
@@ -388,7 +388,7 @@ SETTINGS mutations_sync = 1;
 
 Let's run the same 3 queries.
 
-[Enable](../../operations/settings/settings.md#allow-experimental-projection-optimization) projections for selects:
+[Enable](../operations/settings/settings.md#allow-experimental-projection-optimization) projections for selects:
 
 ```sql
 SET allow_experimental_projection_optimization = 1;

docs/en/install.md

@@ -216,7 +216,7 @@ Use the `clickhouse client` to connect to the server, or `clickhouse local` to p
 
 ### From Sources {#from-sources}
 
-To manually compile ClickHouse, follow the instructions for [Linux](../development/build.md) or [Mac OS X](../development/build-osx.md).
+To manually compile ClickHouse, follow the instructions for [Linux](./development/build.md) or [Mac OS X](./development/build-osx.md).
 
 You can compile packages and install them or use programs without installing packages. Also by building manually you can disable SSE 4.2 requirement or build for AArch64 CPUs.
 
@@ -271,7 +271,7 @@ If the configuration file is in the current directory, you do not need to specif
 
 ClickHouse supports access restriction settings. They are located in the `users.xml` file (next to `config.xml`).
 By default, access is allowed from anywhere for the `default` user, without a password. See `user/default/networks`.
-For more information, see the section [“Configuration Files”](../operations/configuration-files.md).
+For more information, see the section [“Configuration Files”](./operations/configuration-files.md).
 
 After launching server, you can use the command-line client to connect to it:
 
@@ -282,7 +282,7 @@ $ clickhouse-client
 By default, it connects to `localhost:9000` on behalf of the user `default` without a password. It can also be used to connect to a remote server using `--host` argument.
 
 The terminal must use UTF-8 encoding.
-For more information, see the section [“Command-line client”](../interfaces/cli.md).
+For more information, see the section [“Command-line client”](./interfaces/cli.md).
 
 Example:
 

docs/en/interfaces/cli.md

@@ -7,7 +7,7 @@ sidebar_label: Command-Line Client
 
 ClickHouse provides a native command-line client: `clickhouse-client`. The client supports command-line options and configuration files. For more information, see [Configuring](#interfaces_cli_configuration).
 
-[Install](../getting-started/index.md) it from the `clickhouse-client` package and run it with the command `clickhouse-client`.
+[Install](../../quick-start.mdx) it from the `clickhouse-client` package and run it with the command `clickhouse-client`.
 
 ``` bash
 $ clickhouse-client

docs/en/operations/optimizing-performance/sampling-query-profiler.md

@@ -21,7 +21,7 @@ The default sampling frequency is one sample per second and both CPU and real ti
 
 To analyze the `trace_log` system table:
 
--   Install the `clickhouse-common-static-dbg` package. See [Install from DEB Packages](../../getting-started/install.md#install-from-deb-packages).
+-   Install the `clickhouse-common-static-dbg` package. See [Install from DEB Packages](../../install.md#install-from-deb-packages).
 
 -   Allow introspection functions by the [allow_introspection_functions](../../operations/settings/settings.md#settings-allow_introspection_functions) setting.
 

docs/en/operations/performance-test.md

@@ -59,7 +59,7 @@ wget https://raw.githubusercontent.com/ClickHouse/ClickHouse/master/benchmark/cl
 chmod a+x benchmark-new.sh
 wget https://raw.githubusercontent.com/ClickHouse/ClickHouse/master/benchmark/clickhouse/queries.sql
 ```
-3.  Download the [web analytics dataset](../getting-started/example-datasets/metrica.md) (“hits” table containing 100 million rows).
+3.  Download the [web analytics dataset](../example-datasets/metrica.md) (“hits” table containing 100 million rows).
 ```bash
 wget https://datasets.clickhouse.com/hits/partitions/hits_100m_obfuscated_v1.tar.xz
 tar xvf hits_100m_obfuscated_v1.tar.xz -C .

docs/en/operations/requirements.md

@@ -3,7 +3,7 @@ sidebar_position: 44
 sidebar_label: Requirements
 ---
 
-# Requirements {#requirements}
+# Requirements 
 
 ## CPU {#cpu}
 
@@ -56,4 +56,4 @@ The network bandwidth is critical for processing distributed queries with a larg
 
 ClickHouse is developed primarily for the Linux family of operating systems. The recommended Linux distribution is Ubuntu. The `tzdata` package should be installed in the system.
 
-ClickHouse can also work in other operating system families. See details in the [Getting started](../getting-started/index.md) section of the documentation.
+ClickHouse can also work in other operating system families. See details in the [install guide](../install.md) section of the documentation.

docs/en/operations/troubleshooting.md

@@ -3,7 +3,7 @@ sidebar_position: 46
 sidebar_label: Troubleshooting
 ---
 
-# Troubleshooting {#troubleshooting}
+# Troubleshooting 
 
 -   [Installation](#troubleshooting-installation-errors)
 -   [Connecting to the server](#troubleshooting-accepts-no-connections)
@@ -15,7 +15,7 @@ sidebar_label: Troubleshooting
 ### You Cannot Get Deb Packages from ClickHouse Repository with Apt-get {#you-cannot-get-deb-packages-from-clickhouse-repository-with-apt-get}
 
 -   Check firewall settings.
--   If you cannot access the repository for any reason, download packages as described in the [Getting started](../getting-started/index.md) article and install them manually using the `sudo dpkg -i <packages>` command. You will also need the `tzdata` package.
+-   If you cannot access the repository for any reason, download packages as described in the [install guide](../install.md) article and install them manually using the `sudo dpkg -i <packages>` command. You will also need the `tzdata` package.
 
 ## Connecting to the Server {#troubleshooting-accepts-no-connections}
 

docs/en/playground.md

@@ -11,7 +11,7 @@ slug: /en/getting-started/playground
 [ClickHouse Playground](https://play.clickhouse.com/play?user=play) allows people to experiment with ClickHouse by running queries instantly, without setting up their server or cluster.
 Several example datasets are available in Playground.
 
-You can make queries to Playground using any HTTP client, for example [curl](https://curl.haxx.se) or [wget](https://www.gnu.org/software/wget/), or set up a connection using [JDBC](../interfaces/jdbc.md) or [ODBC](../interfaces/odbc.md) drivers. More information about software products that support ClickHouse is available [here](../interfaces/index.md).
+You can make queries to Playground using any HTTP client, for example [curl](https://curl.haxx.se) or [wget](https://www.gnu.org/software/wget/), or set up a connection using [JDBC](./interfaces/jdbc.md) or [ODBC](./interfaces/odbc.md) drivers. More information about software products that support ClickHouse is available [here](./interfaces/index.md).
 
 ## Credentials {#credentials}
 
@@ -39,7 +39,7 @@ HTTPS endpoint example with `curl`:
 curl "https://play.clickhouse.com/?user=explorer" --data-binary "SELECT 'Play ClickHouse'"
 ```
 
-TCP endpoint example with [CLI](../interfaces/cli.md):
+TCP endpoint example with [CLI](./interfaces/cli.md):
 
 ``` bash
 clickhouse client --secure --host play.clickhouse.com --user explorer

docs/en/sql-reference/aggregate-functions/reference/anyheavy.md

@@ -16,7 +16,7 @@ anyHeavy(column)
 
 **Example**
 
-Take the [OnTime](../../../getting-started/example-datasets/ontime.md) data set and select any frequently occurring value in the `AirlineID` column.
+Take the [OnTime](../../../example-datasets/ontime.md) data set and select any frequently occurring value in the `AirlineID` column.
 
 ``` sql
 SELECT anyHeavy(AirlineID) AS res

docs/en/sql-reference/aggregate-functions/reference/topk.md

@@ -28,7 +28,7 @@ If the parameter is omitted, default value 10 is used.
 
 **Example**
 
-Take the [OnTime](../../../getting-started/example-datasets/ontime.md) data set and select the three most frequently occurring values in the `AirlineID` column.
+Take the [OnTime](../../../example-datasets/ontime.md) data set and select the three most frequently occurring values in the `AirlineID` column.
 
 ``` sql
 SELECT topK(3)(AirlineID) AS res

docs/en/sql-reference/dictionaries/external-dictionaries/_category_.yml

@@ -4,4 +4,5 @@ collapsible: true
 collapsed: true 
 link:
   type: generated-index
-  title: External Dictionaries
+  title: External Dictionaries
+  slug: /en/sql-reference/dictionaries/external-dictionaries

docs/en/sql-reference/dictionaries/external-dictionaries/external-dicts-dict-hierarchical.md

@@ -5,7 +5,7 @@ sidebar_label: Hierarchical dictionaries
 
 # Hierarchical Dictionaries 
 
-ClickHouse supports hierarchical dictionaries with a [numeric key](../../../sql-reference/dictionaries/external-dictionaries/external-dicts-dict-structure.md#ext_dict-numeric-key).
+ClickHouse supports hierarchical dictionaries with a [numeric key](../../dictionaries/external-dictionaries/external-dicts-dict-structure.md#numeric-key).
 
 Look at the following hierarchical structure:
 
@@ -35,7 +35,7 @@ This hierarchy can be expressed as the following dictionary table.
 
 This table contains a column `parent_region` that contains the key of the nearest parent for the element.
 
-ClickHouse supports the [hierarchical](../../../sql-reference/dictionaries/external-dictionaries/external-dicts-dict-structure.md#hierarchical-dict-attr) property for [external dictionary](../../../sql-reference/dictionaries/external-dictionaries/index.md) attributes. This property allows you to configure the hierarchical dictionary similar to described above.
+ClickHouse supports the [hierarchical](../../../sql-reference/dictionaries/external-dictionaries/external-dicts-dict-structure.md#hierarchical-dict-attr) property for [external dictionary](../../../sql-reference/dictionaries/external-dictionaries/) attributes. This property allows you to configure the hierarchical dictionary similar to described above.
 
 The [dictGetHierarchy](../../../sql-reference/functions/ext-dict-functions.md#dictgethierarchy) function allows you to get the parent chain of an element.
 

docs/en/sql-reference/dictionaries/external-dictionaries/external-dicts-dict-sources.md

@@ -478,7 +478,7 @@ The `table` and `query` fields cannot be used together. And either one of the `t
 
 ClickHouse receives quoting symbols from ODBC-driver and quote all settings in queries to driver, so it’s necessary to set table name accordingly to table name case in database.
 
-If you have a problems with encodings when using Oracle, see the corresponding [F.A.Q.](../../../faq/integration/oracle-odbc.md) item.
+If you have a problems with encodings when using Oracle, see the corresponding [FAQ](../../../../faq/integration/oracle-odbc.md) item.
 
 ### Mysql {#dicts-external_dicts_dict_sources-mysql}
 

docs/en/sql-reference/dictionaries/external-dictionaries/external-dicts-dict-structure.md

@@ -60,7 +60,7 @@ An xml structure can contain either `<id>` or `<key>`. DDL-query must contain si
 You must not describe key as an attribute.
 :::
 
-### Numeric Key {#ext_dict-numeric-key}
+### Numeric Key {#numeric-key}
 
 Type: `UInt64`.
 

docs/en/sql-reference/functions/other-functions.md

@@ -1216,7 +1216,7 @@ SELECT * FROM table WHERE indexHint(<expression>)
 
 **Example**
 
-Here is the example of test data from the table [ontime](../../getting-started/example-datasets/ontime.md).
+Here is the example of test data from the table [ontime](../../example-datasets/ontime.md).
 
 Input table:
 

docs/en/sql-reference/statements/create/table.md

@@ -239,7 +239,7 @@ Codecs:
 
 High compression levels are useful for asymmetric scenarios, like compress once, decompress repeatedly. Higher levels mean better compression and higher CPU usage.
 
-### Specialized Codecs {#create-query-specialized-codecs}
+### Specialized Codecs {#specialized-codecs}
 
 These codecs are designed to make compression more effective by using specific features of data. Some of these codecs do not compress data themself. Instead, they prepare the data for a common purpose codec, which compresses it better than without this preparation.
 

docs/en/sql-reference/statements/select/into-outfile.md

@@ -2,7 +2,7 @@
 sidebar_label: INTO OUTFILE
 ---
 
-# INTO OUTFILE Clause {#into-outfile-clause}
+# INTO OUTFILE Clause
 
 `INTO OUTFILE` clause redirects the result of a `SELECT` query to a file on the **client** side.
 

docs/en/sql-reference/statements/system.md

@@ -67,7 +67,7 @@ SELECT name, status FROM system.dictionaries;
 
 ## RELOAD MODELS {#query_language-system-reload-models}
 
-Reloads all [CatBoost](../../guides/apply-catboost-model.md#applying-catboost-model-in-clickhouse) models if the configuration was updated without restarting the server.
+Reloads all [CatBoost](../../../guides/developer/apply-catboost-model.md) models if the configuration was updated without restarting the server.
 
 **Syntax**