ClickHouse

ClickHouse is a fast open-source column-oriented database management system that allows generating analytical data reports in real-time using SQL queries.

The ClickHouse Wrapper allows you to read and write data from ClickHouse within your Postgres database.

Preparation#

Before you can query ClickHouse, you need to enable the Wrappers extension and store your credentials in Postgres.

Enable Wrappers#

Make sure the wrappers extension is installed on your database:

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create extension if not exists wrappers with schema extensions;

Enable the ClickHouse Wrapper#

Enable the clickhouse_wrapper FDW:

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create foreign data wrapper clickhouse_wrapper  handler click_house_fdw_handler  validator click_house_fdw_validator;

Store your credentials (optional)#

By default, Postgres stores FDW credentials inside pg_catalog.pg_foreign_server in plain text. Anyone with access to this table will be able to view these credentials. Wrappers is designed to work with Vault, which provides an additional level of security for storing credentials. We recommend using Vault to store your credentials.

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-- Save your ClickHouse credential in Vault and retrieve the `key_id`insert into vault.secrets (name, secret)values (  'clickhouse',  'tcp://default:@localhost:9000/default')returning key_id;

Connecting to ClickHouse#

We need to provide Postgres with the credentials to connect to ClickHouse, and any additional options. We can do this using the create server command:

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create server clickhouse_server  foreign data wrapper clickhouse_wrapper  options (    conn_string_id '<key_ID>' -- The Key ID from above.  );

Some connection string examples:

• tcp://user:password@host:9000/clicks?compression=lz4&ping_timeout=42ms
• tcp://default:PASSWORD@abc.eu-west-1.aws.clickhouse.cloud:9440/default?connection_timeout=30s&ping_before_query=false&secure=true

Check more connection string parameters.

Create a schema#

We recommend creating a schema to hold all the foreign tables:

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create schema if not exists clickhouse;

Options#

The following options are available when creating ClickHouse foreign tables:

• table - Source table name in ClickHouse, required

This can also be a subquery enclosed in parentheses, for example,

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table '(select * from my_table)'

Parametrized views#

Parametrized view is also supported in the subquery. In this case, you need to define a column for each parameter and use where to pass values to them. For example,

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create foreign table test_vw (    id bigint,    col1 text,    col2 bigint,    _param1 text,    _param2 bigint  )    server clickhouse_server    options (      table '(select * from my_view(column1=${_param1}, column2=${_param2}))'    );  select * from test_vw where _param1='aaa' and _param2=32;

• rowid_column - Primary key column name, optional for data scan, required for data modify

Entities#

Tables#

The ClickHouse Wrapper supports data reads and writes from ClickHouse tables.

Operations#

Usage#

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create foreign table clickhouse.my_table (  id bigint,  name text)  server clickhouse_server  options (    table 'people'  );

Notes#

• Supports where, order by and limit clause pushdown
• Supports parametrized views in subqueries
• When using rowid_column, it must be specified for data modification operations

Query Pushdown Support#

This FDW supports where, order by and limit clause pushdown, as well as parametrized view (see above).

Supported Data Types#

Limitations#

This section describes important limitations and considerations when using this FDW:

• Full result sets must be transferred from ClickHouse to PostgreSQL
• Large result sets consume significant PostgreSQL memory
• Only basic query clauses (WHERE, ORDER BY, LIMIT) support pushdown
• Limited data type mappings (see Supported Data Types section)
• Materialized views using foreign tables may fail during logical backups

Examples#

Basic Query Example#

This example demonstrates basic ClickHouse table operations.

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-- Run below SQLs on ClickHouse to create source tablecreate table people (  id Int64,  name String)engine=MergeTree()order by id;-- Add some test datainsert into people values (1, 'Luke Skywalker'), (2, 'Leia Organa'), (3, 'Han Solo');

Create foreign table on Postgres database:

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create foreign table clickhouse.people (  id bigint,  name text)  server clickhouse_server  options (    table 'people'  );-- data scanselect * from clickhouse.people;-- data modifyinsert into clickhouse.people values (4, 'Yoda');update clickhouse.people set name = 'Princess Leia' where id = 2;delete from clickhouse.people where id = 3;