ClickHouse/docs/en/operations/named-collections.md

slug	sidebar_position	sidebar_label
/en/operations/named-collections	69	Named collections

Storing details for connecting to external sources in configuration files

Details for connecting to external sources (dictionaries, tables, table functions) can be saved in configuration files and thus simplify the creation of objects and hide credentials from users with only SQL access.

Parameters can be set in XML <format>CSV</format> and overridden in SQL , format = 'TSV'. The parameters in SQL can be overridden using format key = value: compression_method = 'gzip'.

Named collections are stored in the config.xml file of the ClickHouse server in the <named_collections> section and are applied when ClickHouse starts.

Example of configuration:

$ cat /etc/clickhouse-server/config.d/named_collections.xml
<clickhouse>
     <named_collections>
     ...
     </named_collections>
</clickhouse>

Named collections for accessing S3.

The description of parameters see s3 Table Function.

Example of configuration:

<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>

Example of using named collections with the 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);

SELECT count()
FROM s3(s3_mydata, filename = 'test_file.tsv.gz')

┌─count()─┐
│   10000 │
└─────────┘
1 rows in set. Elapsed: 0.279 sec. Processed 10.00 thousand rows, 90.00 KB (35.78 thousand rows/s., 322.02 KB/s.)

Example of using named collections with an 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.

Example of configuration:

<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>
            <on_duplicate_clause>1</on_duplicate_clause>
            <replace_query>1</replace_query>
        </mymysql>
    </named_collections>
</clickhouse>

Example of using named collections with the mysql function

SELECT count() FROM mysql(mymysql, table = 'test');

┌─count()─┐
│       3 │
└─────────┘

Example of using named collections with an 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 │
└─────────┘

Example of using named collections with database with engine MySQL

CREATE DATABASE mydatabase ENGINE = MySQL(mymysql);

SHOW TABLES FROM mydatabase;

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

Example of using named collections with a dictionary with source MySQL

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.

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 remote ClickHouse database

The description of parameters see remote.

Example of configuration:

<clickhouse>
    <named_collections>
        <remote1>
            <host>localhost</host>
            <port>9000</port>
            <database>system</database>
            <user>foo</user>
            <password>secret</password>
        </remote1>
    </named_collections>
</clickhouse>

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