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Named collections

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.

Storing named collections in the system database​

DDL example​

CREATE NAMED COLLECTION name AS
key_1 = 'value',
key_2 = 'value2',
url = 'https://connection.url/'

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:

/etc/clickhouse-server/users.d/user_default.xml
<clickhouse>
  <users>
    <default>
      <password_sha256_hex>65e84be33532fb784c48129675f9eff3a682b27168c0ea744b2cf58ee02337c5</password_sha256_hex replace=true>
      <access_management>1</access_management>
      <named_collection_control>1</named_collection_control>
    </default>
  </users>
</clickhouse>
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.

Storing named collections in configuration files​

XML example​

/etc/clickhouse-server/config.d/named_collections.xml
<clickhouse>
     <named_collections>
        <name>
            <key_1>value</key_1>
            <key_2>value_2</key_2>
            <url>https://connection.url/</url>
        </name>
     </named_collections>
</clickhouse>

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:

ALTER NAMED COLLECTION collection2 SET key1=4, key3='value3'

Remove the key key2 from collection2:

ALTER NAMED COLLECTION collection2 DELETE key2

Change or add the key key1 and delete the key key3 of the collection collection2:

ALTER NAMED COLLECTION collection2 SET key1=4, DELETE key3

Drop the DDL named collection collection2:​

DROP NAMED COLLECTION collection2

Named collections for accessing S3​

The description of parameters see s3 Table Function.

DDL example​

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​

<clickhouse>
    <named_collections>
        <s3_mydata>
            <access_key_id>AKIAIOSFODNN7EXAMPLE</access_key_id>
            <secret_access_key>wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY</secret_access_key>
            <format>CSV</format>
            <url>https://s3.us-east-1.amazonaws.com/yourbucket/mydata/</url>
        </s3_mydata>
    </named_collections>
</clickhouse>

s3() function and S3 Table named collection examples​

Both of the following examples use the same named collection s3_mydata:

s3() function​

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​

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.

DDL example​

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​

<clickhouse>
    <named_collections>
        <mymysql>
            <user>myuser</user>
            <password>mypass</password>
            <host>127.0.0.1</host>
            <port>3306</port>
            <database>test</database>
            <connection_pool_size>8</connection_pool_size>
            <replace_query>1</replace_query>
        </mymysql>
    </named_collections>
</clickhouse>

mysql() function, MySQL table, MySQL database, and Dictionary named collection examples​

The four following examples use the same named collection mymysql:

mysql() function​

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​

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​

CREATE DATABASE mydatabase ENGINE = MySQL(mymysql);

SHOW TABLES FROM mydatabase;

┌─name───┐
│ source │
│ test   │
└────────┘

MySQL Dictionary​

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.

CREATE NAMED COLLECTION mypg AS
user = 'pguser',
password = 'jw8s0F4',
host = '127.0.0.1',
port = 5432,
database = 'test',
schema = 'test_schema',
connection_pool_size = 8

Example of configuration:

<clickhouse>
    <named_collections>
        <mypg>
            <user>pguser</user>
            <password>jw8s0F4</password>
            <host>127.0.0.1</host>
            <port>5432</port>
            <database>test</database>
            <schema>test_schema</schema>
            <connection_pool_size>8</connection_pool_size>
        </mypg>
    </named_collections>
</clickhouse>

Example of using named collections with the postgresql function​

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​

CREATE TABLE mypgtable (a Int64) ENGINE = PostgreSQL(mypg, table = 'test', schema = 'public');

SELECT * FROM mypgtable;

┌─a─┐
│ 1 │
│ 2 │
│ 3 │
└───┘

Example of using named collections with database with engine PostgreSQL​

CREATE DATABASE mydatabase ENGINE = PostgreSQL(mypg);

SHOW TABLES FROM mydatabase

┌─name─┐
│ test │
└──────┘

Example of using named collections with a dictionary with source POSTGRESQL​

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.

Example of configuration:

CREATE NAMED COLLECTION remote1 AS
host = 'remote_host',
port = 9000,
database = 'system',
user = 'foo',
password = 'secret',
secure = 1
<clickhouse>
    <named_collections>
        <remote1>
            <host>remote_host</host>
            <port>9000</port>
            <database>system</database>
            <user>foo</user>
            <password>secret</password>
            <secure>1</secure>
        </remote1>
    </named_collections>
</clickhouse>

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​

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​

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                       │
└─────────────────────────┘