Consolidate dictionary docs

docs/cloud/reference/01_changelog/01_changelog.md

@@ -1524,7 +1524,7 @@ This release enables dictionaries from local ClickHouse table and HTTP sources,
 
 ### General changes {#general-changes-5}
 
-- Added support for [dictionaries](/sql-reference/dictionaries/index.md) from local ClickHouse table and HTTP sources
+- Added support for [dictionaries](/sql-reference/statements/create/dictionary) from local ClickHouse table and HTTP sources
 - Introduced support for the Mumbai [region](/cloud/reference/supported-regions)
 
 ### Console changes {#console-changes-30}

docs/dictionary/index.md

@@ -3,7 +3,7 @@
 title: 'Dictionary'
 keywords: ['dictionary', 'dictionaries']
 description: 'A dictionary provides a key-value representation of data for fast lookups.'
-doc_type: 'reference'
+doc_type: 'guide'
 ---
 
 import dictionaryUseCases from '@site/static/images/dictionary/dictionary-use-cases.png';
@@ -12,7 +12,7 @@
 
 # Dictionary
 
-A dictionary in ClickHouse provides an in-memory [key-value](https://en.wikipedia.org/wiki/Key%E2%80%93value_database) representation of data from various [internal and external sources](/sql-reference/dictionaries#dictionary-sources), optimizing for super-low latency lookup queries.
+A dictionary in ClickHouse provides an in-memory [key-value](https://en.wikipedia.org/wiki/Key%E2%80%93value_database) representation of data from various [internal and external sources](/sql-reference/statements/create/dictionary/sources#dictionary-sources), optimizing for super-low latency lookup queries.
 
 Dictionaries are useful for:
 - Improving the performance of queries, especially when used with `JOIN`s
@@ -86,7 +86,7 @@
 
 #### Applying a dictionary {#applying-a-dictionary}
 
-To demonstrate these concepts, we use a dictionary for our vote data. Since dictionaries are usually held in memory ([ssd_cache](/sql-reference/dictionaries#ssd_cache) is the exception), you should be cognizant of the size of the data. Confirming our `votes` table size:
+To demonstrate these concepts, we use a dictionary for our vote data. Since dictionaries are usually held in memory ([ssd_cache](/sql-reference/statements/create/dictionary/layouts/ssd-cache) is the exception), you should be cognizant of the size of the data. Confirming our `votes` table size:
 
 ```sql
 SELECT table,
@@ -104,7 +104,7 @@
 
 Data will be stored uncompressed in our dictionary, so we need at least 4GB of memory if we were to store all columns (we won't) in a dictionary. The dictionary will be replicated across our cluster, so this amount of memory needs to be reserved *per node*.
 
-> In the example below the data for our dictionary originates from a ClickHouse table. While this represents the most common source of dictionaries, [a number of sources](/sql-reference/dictionaries#dictionary-sources) are supported including files, http and databases including [Postgres](/sql-reference/dictionaries#postgresql). As we'll show, dictionaries can be automatically refreshed providing an ideal way to ensure small datasets subject to frequent changes are available for direct joins.
+> In the example below the data for our dictionary originates from a ClickHouse table. While this represents the most common source of dictionaries, [a number of sources](/sql-reference/statements/create/dictionary/sources#dictionary-sources) are supported including files, http and databases including [Postgres](/sql-reference/statements/create/dictionary/sources/postgresql). As we'll show, dictionaries can be automatically refreshed providing an ideal way to ensure small datasets subject to frequent changes are available for direct joins.
 
 Our dictionary requires a primary key on which lookups will be performed. This is conceptually identical to a transactional database primary key and should be unique. Our above query requires a lookup on the join key - `PostId`. The dictionary should in turn be populated with the total of the up and down votes per `PostId` from our `votes` table. Here's the query to obtain this dictionary data:
 
@@ -316,20 +316,20 @@
 
 ### Choosing the Dictionary `LAYOUT` {#choosing-the-dictionary-layout}
 
-The `LAYOUT` clause controls the internal data structure for the dictionary. A number of options exist and are documented [here](/sql-reference/dictionaries#ways-to-store-dictionaries-in-memory). Some tips on choosing the correct layout can be found [here](https://clickhouse.com/blog/faster-queries-dictionaries-clickhouse#choosing-a-layout).
+The `LAYOUT` clause controls the internal data structure for the dictionary. A number of options exist and are documented [here](/sql-reference/statements/create/dictionary/layouts#ways-to-store-dictionaries-in-memory). Some tips on choosing the correct layout can be found [here](https://clickhouse.com/blog/faster-queries-dictionaries-clickhouse#choosing-a-layout).
 
 ### Refreshing dictionaries {#refreshing-dictionaries}
 
 We have specified a `LIFETIME` for the dictionary of `MIN 600 MAX 900`. LIFETIME is the update interval for the dictionary, with the values here causing a periodic reload at a random interval between 600 and 900s. This random interval is necessary in order to distribute the load on the dictionary source when updating on a large number of servers. During updates, the old version of a dictionary can still be queried, with only the initial load blocking queries. Note that setting `(LIFETIME(0))` prevents dictionaries from updating.
 Dictionaries can be forcibly reloaded using the `SYSTEM RELOAD DICTIONARY` command.
 
-For database sources such as ClickHouse and Postgres, you can set up a query that will update the dictionaries only if they really changed (the response of the query determines this), rather than at a periodic interval. Further details can be found [here](/sql-reference/dictionaries#refreshing-dictionary-data-using-lifetime).
+For database sources such as ClickHouse and Postgres, you can set up a query that will update the dictionaries only if they really changed (the response of the query determines this), rather than at a periodic interval. Further details can be found [here](/sql-reference/statements/create/dictionary/lifetime#refreshing-dictionary-data-using-lifetime).
 
 ### Other dictionary types {#other-dictionary-types}
 
-ClickHouse also supports [Hierarchical](/sql-reference/dictionaries#hierarchical-dictionaries), [Polygon](/sql-reference/dictionaries#polygon-dictionaries) and [Regular Expression](/sql-reference/dictionaries#regexp-tree-dictionary) dictionaries.
+ClickHouse also supports [Hierarchical](/sql-reference/statements/create/dictionary/layouts/hierarchical), [Polygon](/sql-reference/statements/create/dictionary/layouts/polygon) and [Regular Expression](/sql-reference/statements/create/dictionary/layouts/regexp-tree) dictionaries.
 
 ### More reading {#more-reading}
 
 - [Using Dictionaries to Accelerate Queries](https://clickhouse.com/blog/faster-queries-dictionaries-clickhouse)
-- [Advanced Configuration for Dictionaries](/sql-reference/dictionaries)
+- [Advanced Configuration for Dictionaries](/sql-reference/statements/create/dictionary)

docs/getting-started/example-datasets/cell-towers.md

@@ -166,7 +166,7 @@ SELECT mcc, count() FROM cell_towers GROUP BY mcc ORDER BY count() DESC LIMIT 10
 
 Based on the above query and the [MCC list](https://en.wikipedia.org/wiki/Mobile_country_code), the countries with the most cell towers are: the USA, Germany, and Russia.
 
-You may want to create a [Dictionary](../../sql-reference/dictionaries/index.md) in ClickHouse to decode these values.
+You may want to create a [Dictionary](../../sql-reference/statements/create/dictionary/index.md) in ClickHouse to decode these values.
 
 ## Use case: incorporate geo data {#use-case}
 

docs/integrations/data-sources/cassandra.md

@@ -9,4 +9,4 @@ doc_type: 'reference'
 
 # Cassandra integration
 
-You can integrate with Cassandra via a dictionary. Further details [here](/sql-reference/dictionaries#cassandra).
+You can integrate with Cassandra via a dictionary. Further details [here](/sql-reference/statements/create/dictionary/sources/cassandra).

docs/integrations/tools/data-integration/pg_clickhouse/tutorial.md

@@ -505,7 +505,7 @@ Here's an excerpt from the CSV file you're using in table format. The
     Setting `LIFETIME` to 0 disables automatic updates to avoid unnecessary
     traffic to our S3 bucket. In other cases, you might configure it
     differently. For details, see [Refreshing dictionary data using
-    LIFETIME](/sql-reference/dictionaries#refreshing-dictionary-data-using-lifetime).
+    LIFETIME](/sql-reference/statements/create/dictionary/lifetime#refreshing-dictionary-data-using-lifetime).
     :::
 
     2.  Now import it:
@@ -642,13 +642,13 @@ should only retrieve the columns you actually need.
     "PostgreSQL Client Applications: psql"
   [EXPLAIN]: https://www.postgresql.org/docs/current/sql-explain.html
     "SQL Commands: EXPLAIN"
-  [dictionary]: /sql-reference/dictionaries/index.md
+  [dictionary]: /sql-reference/statements/create/dictionary
   [PGXN]: https://pgxn.org/dist/pg_clickhouse "pg_clickhouse on PGXN"
   [GitHub]: https://github.com/ClickHouse/pg_clickhouse/releases
     "pg_clickhouse Releases on GitHub"
   [pg_clickhouse image]: https://github.com/ClickHouse/pg_clickhouse/pkgs/container/pg_clickhouse
     "pg_clickhouse OCI Image on GitHub"
   [Postgres image]: https://hub.docker.com/_/postgres
     "Postgres OCI Image on Docker Hub"
-  [Refreshing dictionary data using LIFETIME]: /sql-reference/dictionaries/index.md#refreshing-dictionary-data-using-lifetime
+  [Refreshing dictionary data using LIFETIME]: /sql-reference/statements/create/dictionary/lifetime#refreshing-dictionary-data-using-lifetime
     "ClickHouse Doc: Refreshing dictionary data using LIFETIME"

docs/tutorial.md

@@ -365,7 +365,7 @@
 
 ## Create a dictionary {#create-a-dictionary}
 
-A dictionary is a mapping of key-value pairs stored in memory. For details, see [Dictionaries](/sql-reference/dictionaries/index.md) 
+A dictionary is a mapping of key-value pairs stored in memory. For details, see [Dictionaries](/sql-reference/statements/create/dictionary) 
 
 Create a dictionary associated with a table in your ClickHouse service.
 The table and dictionary are based on a CSV file that contains a row for each neighborhood in New York City. 
@@ -398,7 +398,7 @@
   ```
 
   :::note
-  Setting `LIFETIME` to 0 disables automatic updates to avoid unnecessary traffic to our S3 bucket. In other cases, you might configure it differently. For details, see [Refreshing dictionary data using LIFETIME](/sql-reference/dictionaries#refreshing-dictionary-data-using-lifetime).
+  Setting `LIFETIME` to 0 disables automatic updates to avoid unnecessary traffic to our S3 bucket. In other cases, you might configure it differently. For details, see [Refreshing dictionary data using LIFETIME](/sql-reference/statements/create/dictionary/lifetime#refreshing-dictionary-data-using-lifetime).
   :::
 
 3. Verify it worked. The following should return 265 rows, or one row for each neighborhood:

docs/use-cases/observability/build-your-own/schema-design.md

@@ -578,7 +578,7 @@
 
 ## Using dictionaries {#using-dictionaries}
 
-[Dictionaries](/sql-reference/dictionaries) are a [key feature](https://clickhouse.com/blog/faster-queries-dictionaries-clickhouse) of ClickHouse providing in-memory [key-value](https://en.wikipedia.org/wiki/Key%E2%80%93value_database) representation of data from various internal and external [sources](/sql-reference/dictionaries#dictionary-sources), optimized for super-low latency lookup queries.
+[Dictionaries](/sql-reference/statements/create/dictionary) are a [key feature](https://clickhouse.com/blog/faster-queries-dictionaries-clickhouse) of ClickHouse providing in-memory [key-value](https://en.wikipedia.org/wiki/Key%E2%80%93value_database) representation of data from various internal and external [sources](/sql-reference/statements/create/dictionary/sources#dictionary-sources), optimized for super-low latency lookup queries.
 
 <Image img={observability_12} alt="Observability and dictionaries" size="md"/>
 
@@ -715,7 +715,7 @@
 FROM geoip_url
 ```
 
-In order to perform low-latency IP lookups in ClickHouse, we'll leverage dictionaries to store key -> attributes mapping for our Geo IP data in-memory. ClickHouse provides an `ip_trie` [dictionary structure](/sql-reference/dictionaries#ip_trie) to map our network prefixes (CIDR blocks) to coordinates and country codes. The following query specifies a dictionary using this layout and the above table as the source.
+In order to perform low-latency IP lookups in ClickHouse, we'll leverage dictionaries to store key -> attributes mapping for our Geo IP data in-memory. ClickHouse provides an `ip_trie` [dictionary structure](/sql-reference/statements/create/dictionary/layouts/ip-trie) to map our network prefixes (CIDR blocks) to coordinates and country codes. The following query specifies a dictionary using this layout and the above table as the source.
 
 ```sql
 CREATE DICTIONARY ip_trie (
@@ -825,10 +825,10 @@
 
 The parsing of [user agent strings](https://en.wikipedia.org/wiki/User_agent) is a classical regular expression problem and a common requirement in log and trace based datasets. ClickHouse provides efficient parsing of user agents using Regular Expression Tree Dictionaries.
 
-Regular expression tree dictionaries are defined in ClickHouse open-source using the YAMLRegExpTree dictionary source type which provides the path to a YAML file containing the regular expression tree. Should you wish to provide your own regular expression dictionary, the details on the required structure can be found [here](/sql-reference/dictionaries#use-regular-expression-tree-dictionary-in-clickhouse-open-source). Below we focus on user-agent parsing using [uap-core](https://github.com/ua-parser/uap-core) and load our dictionary for the supported CSV format. This approach is compatible with OSS and ClickHouse Cloud.
+Regular expression tree dictionaries are defined in ClickHouse open-source using the YAMLRegExpTree dictionary source type which provides the path to a YAML file containing the regular expression tree. Should you wish to provide your own regular expression dictionary, the details on the required structure can be found [here](/sql-reference/statements/create/dictionary/layouts/regexp-tree#use-regular-expression-tree-dictionary-in-clickhouse-open-source). Below we focus on user-agent parsing using [uap-core](https://github.com/ua-parser/uap-core) and load our dictionary for the supported CSV format. This approach is compatible with OSS and ClickHouse Cloud.
 
 :::note
-In the examples below, we use snapshots of the latest uap-core regular expressions for user-agent parsing from June 2024. The latest file, which is occasionally updated, can be found [here](https://raw.githubusercontent.com/ua-parser/uap-core/master/regexes.yaml). You can follow the steps [here](/sql-reference/dictionaries#collecting-attribute-values) to load into the CSV file used below.
+In the examples below, we use snapshots of the latest uap-core regular expressions for user-agent parsing from June 2024. The latest file, which is occasionally updated, can be found [here](https://raw.githubusercontent.com/ua-parser/uap-core/master/regexes.yaml). You can follow the steps [here](/sql-reference/statements/create/dictionary/layouts/regexp-tree#collecting-attribute-values) to load into the CSV file used below.
 :::
 
 Create the following Memory tables. These hold our regular expressions for parsing devices, browsers and operating systems.
@@ -1015,7 +1015,7 @@
 
 - [Advanced dictionary topics](/dictionary#advanced-dictionary-topics)
 - ["Using Dictionaries to Accelerate Queries"](https://clickhouse.com/blog/faster-queries-dictionaries-clickhouse)
-- [Dictionaries](/sql-reference/dictionaries)
+- [Dictionaries](/sql-reference/statements/create/dictionary)
 
 ## Accelerating queries {#accelerating-queries}
 

plugins/floating-pages-exceptions.txt

@@ -24,3 +24,4 @@ interfaces/arrowflight
 interfaces/overview
 operations/utilities/clickhouse-keeper-http-api.md
 operations/settings/detach-non-readonly-queries.md
+sql-reference/dictionaries/index.md

scripts/sed_links.sh

@@ -18,7 +18,7 @@ if [[ "$OSTYPE" == "darwin"* ]]; then
     sed -i '' 's|(https://clickhouse.com/docs/sql-reference/statements/select#apply)|(/sql-reference/statements/select)|g' docs/guides/developer/dynamic-column-selection.md
     sed -i '' 's|(/sql-reference/statements/select#replace)|(/sql-reference/statements/select)|g' docs/guides/developer/dynamic-column-selection.md
     sed -i '' 's|(/sql-reference/statements/select#except)|(/sql-reference/statements/select)|g' docs/guides/developer/dynamic-column-selection.md
-    sed -i '' 's|(/cloud/reference/cloud-compatibility.md)|(/whats-new/cloud-compatibility)|g' docs/sql-reference/dictionaries/_snippet_dictionary_in_cloud.md
+    sed -i '' 's|(/cloud/reference/cloud-compatibility.md)|(/whats-new/cloud-compatibility)|g' docs/sql-reference/statements/create/dictionary/_snippet_dictionary_in_cloud.md
     sed -i '' 's|(/cloud/security/secure-s3)|(/cloud/data-sources/secure-s3)|g' docs/engines/table-engines/integrations/s3queue.md
     sed -i '' 's|(/cloud/security/cloud-access-management/overview#initial-settings)|(/cloud/security/console-roles)|g' docs/sql-reference/statements/grant.md
     sed -i '' 's|(/cloud/security/secure-s3#access-your-s3-bucket-with-the-clickhouseaccess-role)|(/cloud/data-sources/secure-s3#access-your-s3-bucket-with-the-clickhouseaccess-role)|g' docs/sql-reference/table-functions/s3.md
@@ -32,7 +32,7 @@ else
     sed -i 's|(https://clickhouse.com/docs/sql-reference/statements/select#apply)|(/sql-reference/statements/select)|g' docs/guides/developer/dynamic-column-selection.md
     sed -i 's|(/sql-reference/statements/select#replace)|(/sql-reference/statements/select)|g' docs/guides/developer/dynamic-column-selection.md
     sed -i 's|(/sql-reference/statements/select#except)|(/sql-reference/statements/select)|g' docs/guides/developer/dynamic-column-selection.md
-    sed -i 's|(/cloud/reference/cloud-compatibility.md)|(/whats-new/cloud-compatibility)|g' docs/sql-reference/dictionaries/_snippet_dictionary_in_cloud.md
+    sed -i 's|(/cloud/reference/cloud-compatibility.md)|(/whats-new/cloud-compatibility)|g' docs/sql-reference/statements/create/dictionary/_snippet_dictionary_in_cloud.md
     sed -i 's|(/cloud/security/secure-s3)|(/cloud/data-sources/secure-s3)|g' docs/engines/table-engines/integrations/s3queue.md
     sed -i 's|(/cloud/security/cloud-access-management/overview#initial-settings)|(/cloud/security/console-roles)|g' docs/sql-reference/statements/grant.md
     sed -i 's|(/cloud/security/secure-s3#access-your-s3-bucket-with-the-clickhouseaccess-role)|(/cloud/data-sources/secure-s3#access-your-s3-bucket-with-the-clickhouseaccess-role)|g' docs/sql-reference/table-functions/s3.md

sidebars.js

@@ -1241,7 +1241,7 @@ const sidebars = {
           label: 'Dictionary',
           collapsible: true,
           collapsed: true,
-          items: ['dictionary/index', 'sql-reference/dictionaries/index'],
+          items: ['dictionary/index'],
         },
         {
           type: 'category',

src/theme/badges/CloudNotSupportedBadge/index.js

@@ -8,7 +8,6 @@ const Icon = () => {
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             <path strokeWidth="1.5" d="M2.66699 14L12.0003 4.66663" stroke="#660099" strokeLinecap="round" strokeLinejoin="round"/>
             </svg>
-
         </div>
     )
 }