--- slug: /en/operations/named-collections sidebar_position: 69 sidebar_label: "Named collections" title: "Named collections" --- import CloudNotSupportedBadge from '@theme/badges/CloudNotSupportedBadge'; Named collections provide a way to store collections of key-value pairs to be used to configure integrations with external sources. You can use named collections with dictionaries, tables, table functions, and object storage. Named collections can be configured with DDL or in configuration files and are applied when ClickHouse starts. They simplify the creation of objects and the hiding of credentials from users without administrative access. The keys in a named collection must match the parameter names of the corresponding function, table engine, database, etc. In the examples below the parameter list is linked to for each type. Parameters set in a named collection can be overridden in SQL, this is shown in the examples below. This ability can be limited using `[NOT] OVERRIDABLE` keywords and XML attributes and/or the configuration option `allow_named_collection_override_by_default`. :::warning If override is allowed, it may be possible for users without administrative access to figure out the credentials that you are trying to hide. If you are using named collections with that purpose, you should disable `allow_named_collection_override_by_default` (which is enabled by default). ::: ## Storing named collections in the system database ### DDL example ```sql CREATE NAMED COLLECTION name AS key_1 = 'value' OVERRIDABLE, key_2 = 'value2' NOT OVERRIDABLE, url = 'https://connection.url/' ``` In the above example: * `key_1` can always be overridden. * `key_2` can never be overridden. * `url` can be overridden or not depending on the value of `allow_named_collection_override_by_default`. ### Permissions to create named collections with DDL To manage named collections with DDL a user must have the `named_control_collection` privilege. This can be assigned by adding a file to `/etc/clickhouse-server/users.d/`. The example gives the user `default` both the `access_management` and `named_collection_control` privileges: ```xml title='/etc/clickhouse-server/users.d/user_default.xml' 65e84be33532fb784c48129675f9eff3a682b27168c0ea744b2cf58ee02337c5 1 1 ``` :::tip In the above example the `password_sha256_hex` value is the hexadecimal representation of the SHA256 hash of the password. This configuration for the user `default` has the attribute `replace=true` as in the default configuration has a plain text `password` set, and it is not possible to have both plain text and sha256 hex passwords set for a user. ::: ### Storage for named collections Named collections can either be stored on local disk or in ZooKeeper/Keeper. By default local storage is used. They can also be stored using encryption with the same algorithms used for [disk encryption](storing-data#encrypted-virtual-file-system), where `aes_128_ctr` is used by default. To configure named collections storage you need to specify a `type`. This can be either `local` or `keeper`/`zookeeper`. For encrypted storage, you can use `local_encrypted` or `keeper_encrypted`/`zookeeper_encrypted`. To use ZooKeeper/Keeper we also need to set up a `path` (path in ZooKeeper/Keeper, where named collections will be stored) to `named_collections_storage` section in configuration file. The following example uses encryption and ZooKeeper/Keeper: ``` zookeeper_encrypted bebec0cabebec0cabebec0cabebec0ca aes_128_ctr /named_collections_path/ 1000 ``` An optional configuration parameter `update_timeout_ms` by default is equal to `5000`. ## Storing named collections in configuration files ### XML example ```xml title='/etc/clickhouse-server/config.d/named_collections.xml' value value_2 https://connection.url/ ``` In the above example: * `key_1` can always be overridden. * `key_2` can never be overridden. * `url` can be overridden or not depending on the value of `allow_named_collection_override_by_default`. ## Modifying named collections Named collections that are created with DDL queries can be altered or dropped with DDL. Named collections created with XML files can be managed by editing or deleting the corresponding XML. ### Alter a DDL named collection Change or add the keys `key1` and `key3` of the collection `collection2` (this will not change the value of the `overridable` flag for those keys): ```sql ALTER NAMED COLLECTION collection2 SET key1=4, key3='value3' ``` Change or add the key `key1` and allow it to be always overridden: ```sql ALTER NAMED COLLECTION collection2 SET key1=4 OVERRIDABLE ``` Remove the key `key2` from `collection2`: ```sql ALTER NAMED COLLECTION collection2 DELETE key2 ``` Change or add the key `key1` and delete the key `key3` of the collection `collection2`: ```sql ALTER NAMED COLLECTION collection2 SET key1=4, DELETE key3 ``` To force a key to use the default settings for the `overridable` flag, you have to remove and re-add the key. ```sql ALTER NAMED COLLECTION collection2 DELETE key1; ALTER NAMED COLLECTION collection2 SET key1=4; ``` ### Drop the DDL named collection `collection2`: ```sql DROP NAMED COLLECTION collection2 ``` ## Named collections for accessing S3 The description of parameters see [s3 Table Function](../sql-reference/table-functions/s3.md). ### DDL example ```sql CREATE NAMED COLLECTION s3_mydata AS access_key_id = 'AKIAIOSFODNN7EXAMPLE', secret_access_key = 'wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY', format = 'CSV', url = 'https://s3.us-east-1.amazonaws.com/yourbucket/mydata/' ``` ### XML example ```xml AKIAIOSFODNN7EXAMPLE wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY CSV https://s3.us-east-1.amazonaws.com/yourbucket/mydata/ ``` ### s3() function and S3 Table named collection examples Both of the following examples use the same named collection `s3_mydata`: #### s3() function ```sql INSERT INTO FUNCTION s3(s3_mydata, filename = 'test_file.tsv.gz', format = 'TSV', structure = 'number UInt64', compression_method = 'gzip') SELECT * FROM numbers(10000); ``` :::tip The first argument to the `s3()` function above is the name of the collection, `s3_mydata`. Without named collections, the access key ID, secret, format, and URL would all be passed in every call to the `s3()` function. ::: #### S3 table ```sql CREATE TABLE s3_engine_table (number Int64) ENGINE=S3(s3_mydata, url='https://s3.us-east-1.amazonaws.com/yourbucket/mydata/test_file.tsv.gz', format = 'TSV') SETTINGS input_format_with_names_use_header = 0; SELECT * FROM s3_engine_table LIMIT 3; ┌─number─┐ │ 0 │ │ 1 │ │ 2 │ └────────┘ ``` ## Named collections for accessing MySQL database The description of parameters see [mysql](../sql-reference/table-functions/mysql.md). ### DDL example ```sql CREATE NAMED COLLECTION mymysql AS user = 'myuser', password = 'mypass', host = '127.0.0.1', port = 3306, database = 'test', connection_pool_size = 8, replace_query = 1 ``` ### XML example ```xml myuser mypass 127.0.0.1 3306 test 8 1 ``` ### mysql() function, MySQL table, MySQL database, and Dictionary named collection examples The four following examples use the same named collection `mymysql`: #### mysql() function ```sql SELECT count() FROM mysql(mymysql, table = 'test'); ┌─count()─┐ │ 3 │ └─────────┘ ``` :::note The named collection does not specify the `table` parameter, so it is specified in the function call as `table = 'test'`. ::: #### MySQL table ```sql CREATE TABLE mytable(A Int64) ENGINE = MySQL(mymysql, table = 'test', connection_pool_size=3, replace_query=0); SELECT count() FROM mytable; ┌─count()─┐ │ 3 │ └─────────┘ ``` :::note The DDL overrides the named collection setting for connection_pool_size. ::: #### MySQL database ```sql CREATE DATABASE mydatabase ENGINE = MySQL(mymysql); SHOW TABLES FROM mydatabase; ┌─name───┐ │ source │ │ test │ └────────┘ ``` #### MySQL Dictionary ```sql CREATE DICTIONARY dict (A Int64, B String) PRIMARY KEY A SOURCE(MYSQL(NAME mymysql TABLE 'source')) LIFETIME(MIN 1 MAX 2) LAYOUT(HASHED()); SELECT dictGet('dict', 'B', 2); ┌─dictGet('dict', 'B', 2)─┐ │ two │ └─────────────────────────┘ ``` ## Named collections for accessing PostgreSQL database The description of parameters see [postgresql](../sql-reference/table-functions/postgresql.md). Additionally, there are aliases: - `username` for `user` - `db` for `database`. Parameter `addresses_expr` is used in a collection instead of `host:port`. The parameter is optional, because there are other optional ones: `host`, `hostname`, `port`. The following pseudo code explains the priority: ```sql CASE WHEN collection['addresses_expr'] != '' THEN collection['addresses_expr'] WHEN collection['host'] != '' THEN collection['host'] || ':' || if(collection['port'] != '', collection['port'], '5432') WHEN collection['hostname'] != '' THEN collection['hostname'] || ':' || if(collection['port'] != '', collection['port'], '5432') END ``` Example of creation: ```sql CREATE NAMED COLLECTION mypg AS user = 'pguser', password = 'jw8s0F4', host = '127.0.0.1', port = 5432, database = 'test', schema = 'test_schema' ``` Example of configuration: ```xml pguser jw8s0F4 127.0.0.1 5432 test test_schema ``` ### Example of using named collections with the postgresql function ```sql SELECT * FROM postgresql(mypg, table = 'test'); ┌─a─┬─b───┐ │ 2 │ two │ │ 1 │ one │ └───┴─────┘ SELECT * FROM postgresql(mypg, table = 'test', schema = 'public'); ┌─a─┐ │ 1 │ │ 2 │ │ 3 │ └───┘ ``` ### Example of using named collections with database with engine PostgreSQL ```sql CREATE TABLE mypgtable (a Int64) ENGINE = PostgreSQL(mypg, table = 'test', schema = 'public'); SELECT * FROM mypgtable; ┌─a─┐ │ 1 │ │ 2 │ │ 3 │ └───┘ ``` :::note PostgreSQL copies data from the named collection when the table is being created. A change in the collection does not affect the existing tables. ::: ### Example of using named collections with database with engine PostgreSQL ```sql CREATE DATABASE mydatabase ENGINE = PostgreSQL(mypg); SHOW TABLES FROM mydatabase ┌─name─┐ │ test │ └──────┘ ``` ### Example of using named collections with a dictionary with source POSTGRESQL ```sql CREATE DICTIONARY dict (a Int64, b String) PRIMARY KEY a SOURCE(POSTGRESQL(NAME mypg TABLE test)) LIFETIME(MIN 1 MAX 2) LAYOUT(HASHED()); SELECT dictGet('dict', 'b', 2); ┌─dictGet('dict', 'b', 2)─┐ │ two │ └─────────────────────────┘ ``` ## Named collections for accessing a remote ClickHouse database The description of parameters see [remote](../sql-reference/table-functions/remote.md/#parameters). Example of configuration: ```sql CREATE NAMED COLLECTION remote1 AS host = 'remote_host', port = 9000, database = 'system', user = 'foo', password = 'secret', secure = 1 ``` ```xml remote_host 9000 system foo secret 1 ``` `secure` is not needed for connection because of `remoteSecure`, but it can be used for dictionaries. ### Example of using named collections with the `remote`/`remoteSecure` functions ```sql SELECT * FROM remote(remote1, table = one); ┌─dummy─┐ │ 0 │ └───────┘ SELECT * FROM remote(remote1, database = merge(system, '^one')); ┌─dummy─┐ │ 0 │ └───────┘ INSERT INTO FUNCTION remote(remote1, database = default, table = test) VALUES (1,'a'); SELECT * FROM remote(remote1, database = default, table = test); ┌─a─┬─b─┐ │ 1 │ a │ └───┴───┘ ``` ### Example of using named collections with a dictionary with source ClickHouse ```sql CREATE DICTIONARY dict(a Int64, b String) PRIMARY KEY a SOURCE(CLICKHOUSE(NAME remote1 TABLE test DB default)) LIFETIME(MIN 1 MAX 2) LAYOUT(HASHED()); SELECT dictGet('dict', 'b', 1); ┌─dictGet('dict', 'b', 1)─┐ │ a │ └─────────────────────────┘ ``` ## Named collections for accessing Kafka The description of parameters see [Kafka](../engines/table-engines/integrations/kafka.md). ### DDL example ```sql CREATE NAMED COLLECTION my_kafka_cluster AS kafka_broker_list = 'localhost:9092', kafka_topic_list = 'kafka_topic', kafka_group_name = 'consumer_group', kafka_format = 'JSONEachRow', kafka_max_block_size = '1048576'; ``` ### XML example ```xml localhost:9092 kafka_topic consumer_group JSONEachRow 1048576 ``` ### Example of using named collections with a Kafka table Both of the following examples use the same named collection `my_kafka_cluster`: ```sql CREATE TABLE queue ( timestamp UInt64, level String, message String ) ENGINE = Kafka(my_kafka_cluster) CREATE TABLE queue ( timestamp UInt64, level String, message String ) ENGINE = Kafka(my_kafka_cluster) SETTINGS kafka_num_consumers = 4, kafka_thread_per_consumer = 1; ```