Skip to main content

​
Introduction

ClickHouse Connect is a core database driver providing interoperability with a wide range of Python applications.
  • The main interface is the Client object in the package clickhouse_connect.driver. That core package also includes assorted helper classes and utility functions used for communicating with the ClickHouse server and “context” implementations for advanced management of insert and select queries.
  • The clickhouse_connect.datatypes package provides a base implementation and subclasses for all non-experimental ClickHouse datatypes. Its primary functionality is serialization and deserialization of ClickHouse data into the ClickHouse “Native” binary columnar format, used to achieve the most efficient transport between ClickHouse and client applications.
  • The Cython/C classes in the clickhouse_connect.cdriver package optimize some of the most common serializations and deserializations for significantly improved performance over pure Python.
  • There is a SQLAlchemy dialect in the package clickhouse_connect.cc_sqlalchemy which is built off of the datatypes and dbi packages. This implementation supports SQLAlchemy Core functionality including SELECT queries with JOINs (INNER, LEFT OUTER, FULL OUTER, CROSS), WHERE clauses, ORDER BY, LIMIT/OFFSET, DISTINCT operations, lightweight DELETE statements with WHERE conditions, table reflection, and basic DDL operations (CREATE TABLE, CREATE/DROP DATABASE). While it doesn’t support advanced ORM features or advanced DDL features, it provides robust query capabilities suitable for most analytical workloads against ClickHouse’s OLAP-oriented database.
  • The core driver and ClickHouse Connect SQLAlchemy implementation are the preferred method for connecting ClickHouse to Apache Superset. Use the ClickHouse Connect database connection, or clickhousedb SQLAlchemy dialect connection string.
This documentation is current as of the clickhouse-connect release 0.9.2.
The official ClickHouse Connect Python driver uses the HTTP protocol for communication with the ClickHouse server. This enables HTTP load balancer support and works well in enterprise environments with firewalls and proxies, but has slightly lower compression and performance compared to the native TCP-based protocol, and lacks support for some advanced features like query cancellation. For some use cases, you may consider using one of the Community Python drivers that use the native TCP-based protocol.

​
Requirements and compatibility

PythonPlatform¹ClickHouseSQLAlchemy²Apache SupersetPandasPolars
2.x, <3.9❌Linux (x86)✅<25.x³🟡<1.4.40❌<1.4❌≥1.5✅1.x✅
3.9.x✅Linux (Aarch64)✅25.x³🟡≥1.4.40✅1.4.x✅2.x✅
3.10.x✅macOS (x86)✅25.3.x (LTS)✅≥2.x✅1.5.x✅
3.11.x✅macOS (ARM)✅25.6.x (Stable)✅2.0.x✅
3.12.x✅Windows✅25.7.x (Stable)✅2.1.x✅
3.13.x✅25.8.x (LTS)✅3.0.x✅
25.9.x (Stable)✅
¹ClickHouse Connect has been explicitly tested against the listed platforms. In addition, untested binary wheels (with C optimization) are built for all architectures supported by the excellent cibuildwheel project. Finally, because ClickHouse Connect can also run as pure Python, the source installation should work on any recent Python installation. ²SQLAlchemy support is limited to Core functionality (queries, basic DDL). ORM features aren’t supported. See SQLAlchemy Integration Support docs for details. ³ClickHouse Connect generally works well with versions outside the officially supported range.

​
Installation

Install ClickHouse Connect from PyPI via pip: pip install clickhouse-connect ClickHouse Connect can also be installed from source:
  • git clone the GitHub repository.
  • (Optional) run pip install cython to build and enable the C/Cython optimizations
  • cd to the project root directory and run pip install .

​
Support policy

Please update to the latest version of ClickHouse Connect before reporting any issues. Issues should be filed in the GitHub project. Future releases of ClickHouse Connect are intended be compatible with actively supported ClickHouse versions at the time of release. Actively supported versions of ClickHouse server can be found here. If you’re unsure what version of ClickHouse server to use, read this discussion here. Our CI test matrix tests against the latest two LTS releases and the latest three stable releases. However, due to the HTTP protocol and minimal breaking changes between ClickHouse releases, ClickHouse Connect generally works well with server versions outside the officially supported range, though compatibility with certain advanced data types may vary.

​
Basic usage

​
Gather your connection details

To connect to ClickHouse with HTTP(S) you need this information:
Parameter(s)Description
HOST and PORTTypically, the port is 8443 when using TLS or 8123 when not using TLS.
DATABASE NAMEOut of the box, there is a database named default, use the name of the database that you want to connect to.
USERNAME and PASSWORDOut of the box, the username is default. Use the username appropriate for your use case.
The details for your ClickHouse Cloud service are available in the ClickHouse Cloud console. Select a service and click Connect: Choose HTTPS. Connection details are displayed in an example curl command. If you’re using self-managed ClickHouse, the connection details are set by your ClickHouse administrator.

​
Establish a connection

There are two examples shown for connecting to ClickHouse:
  • Connecting to a ClickHouse server on localhost.
  • Connecting to a ClickHouse Cloud service.

​
Use a ClickHouse Connect client instance to connect to a ClickHouse server on localhost:

import clickhouse_connect

client = clickhouse_connect.get_client(host='localhost', username='default', password='password')

​
Use a ClickHouse Connect client instance to connect to a ClickHouse Cloud service:

Use the connection details gathered earlier. ClickHouse Cloud services require TLS, so use port 8443.
import clickhouse_connect

client = clickhouse_connect.get_client(host='HOSTNAME.clickhouse.cloud', port=8443, username='default', password='your password')

​
Interact with your database

To run a ClickHouse SQL command, use the client command method: 
client.command('CREATE TABLE new_table (key UInt32, value String, metric Float64) ENGINE MergeTree ORDER BY key')
To insert batch data, use the client insert method with a two-dimensional array of rows and values: 
row1 = [1000, 'String Value 1000', 5.233]
row2 = [2000, 'String Value 2000', -107.04]
data = [row1, row2]
client.insert('new_table', data, column_names=['key', 'value', 'metric'])
To retrieve data using ClickHouse SQL, use the client query method: 
result = client.query('SELECT max(key), avg(metric) FROM new_table')
print(result.result_rows)
# Output: [(2000, -50.9035)]