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Kanishga Subramani
Kanishga Subramani

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Day 54: Using ClickHouse® Python Clients Effectively: A Practical Guide

#devops #database #dataengineering #clickhouse

Using ClickHouse® Python Clients Effectively: A Practical Guide

Day 54 of #100DaysOfClickHouse

Python has become the language of choice for modern data engineering, analytics, automation, and backend development. Whether you're building an ETL pipeline, powering dashboards, automating reporting, or integrating ClickHouse® into a web application, your Python application needs a reliable way to communicate with the database.

This is where Python clients come into play.

A Python client acts as the bridge between your application and ClickHouse®. It manages the database connection, sends SQL queries, inserts data, retrieves results, and handles communication with the server, allowing developers to focus on building applications instead of implementing low-level networking logic.

Today, two Python libraries are commonly used with ClickHouse®:

  • clickhouse-driver
  • clickhouse-connect

Although both libraries enable Python applications to interact with ClickHouse®, they differ in architecture, capabilities, and recommended use cases.

In this article, we'll explore both clients, compare their features, and discuss best practices for building efficient and scalable Python applications with ClickHouse®.

Why Use a Python Client?

Python applications need a database client to communicate with ClickHouse®.

A client is responsible for:

  • Establishing database connections
  • Executing SQL queries
  • Inserting records
  • Fetching query results
  • Managing communication with the server

Without a client library, developers would need to implement database communication manually, which would be both difficult and unnecessary.

The Evolution of ClickHouse® Python Clients

Before ClickHouse® introduced an official Python client, the community primarily relied on clickhouse-driver.

Built on ClickHouse's native TCP protocol, clickhouse-driver quickly became the preferred solution for production workloads because of its excellent performance, reliability, and mature API.

It continues to power many existing analytics platforms, ETL pipelines, and backend services today.

As ClickHouse® evolved—with features like ClickHouse Cloud, Apache Arrow support, DataFrame integration, and enhanced HTTP APIs—the ClickHouse® team introduced clickhouse-connect, an officially maintained Python client.

Unlike clickhouse-driver, which communicates over the native TCP protocol, clickhouse-connect uses the HTTP/HTTPS interface while offering a modern Python API designed for today's analytics workflows.

Today, both clients are actively maintained, but clickhouse-connect is generally recommended for most new projects.

Comparing the Two Clients

Although both libraries accomplish the same goal, they take slightly different approaches.

Feature clickhouse-driver clickhouse-connect
Maintainer Community Official ClickHouse® Team
Communication Protocol Native TCP HTTP / HTTPS
ClickHouse Cloud Support Good Excellent
Pandas Integration Available Built-in
Apache Arrow Support Limited Excellent
Recommended for New Projects Existing deployments Yes

If you're starting a new application today, clickhouse-connect usually provides the best developer experience.

However, if you're maintaining an existing application built with clickhouse-driver, there's often little reason to migrate unless you specifically need newer features.

Feature Comparison

Here's a more detailed comparison.

Feature clickhouse-driver clickhouse-connect
Maintainer Community Official ClickHouse® Team
Communication Protocol Native TCP HTTP / HTTPS
Recommended for New Projects Good Yes
ClickHouse Cloud Support Supported Excellent
Compression Support Yes Yes
Parameterized Queries Yes Yes
Batch Inserts Yes Yes
Pandas Integration Available Native Support
Apache Arrow Support Limited Excellent
NumPy Support Yes Yes
SQLAlchemy Support Via dialects Supported
Async Support No native async API Yes (get_async_client)
Mature Production Usage Excellent Excellent
Active Development Community Official

Both libraries are production-ready and capable of handling demanding analytical workloads.

Connecting to ClickHouse®

Installing the official client is straightforward.

pip install clickhouse-connect
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Create a client connection.

import clickhouse_connect

client = clickhouse_connect.get_client(
    host="localhost",
    port=8123,
    username="default",
    password="password"
)
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Execute a query.

result = client.query("SELECT version()")

print(result.result_rows)
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If you're using clickhouse-driver, the process is similarly simple.

from clickhouse_driver import Client

client = Client(host="localhost")

result = client.execute("SELECT version()")
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Both libraries provide a clean and intuitive API for Python developers.

Execute Queries Safely

A common mistake is constructing SQL queries using Python string interpolation.

For example:

query = f"""
SELECT *
FROM users
WHERE id = {user_id}
"""
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Instead, use parameterized queries whenever possible.

client.query(
    """
    SELECT *
    FROM users
    WHERE id = {user_id:UInt64}
    """,
    parameters={
        "user_id": user_id
    }
)
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Parameterized queries offer several advantages:

  • Better readability
  • Easier maintenance
  • Reduced risk of malformed SQL
  • Protection against SQL injection

Whenever possible, avoid building SQL through string concatenation.

Batch Your Inserts

ClickHouse® is optimized for analytical workloads and performs best when data is inserted in batches.

Avoid inserting one row at a time.

for row in rows:
    client.insert("events", [row])
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Instead, send multiple rows together.

client.insert(
    "events",
    rows
)
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Batch inserts reduce network overhead, create fewer table parts, and significantly improve ingestion performance.

Collecting records into larger batches is almost always the better approach.

Reuse Your Client

Another common anti-pattern is creating a new database connection for every query.

Instead, create the client once and reuse it throughout your application.

This is especially important for:

  • FastAPI applications
  • Flask services
  • Airflow DAGs
  • Background workers
  • Streaming pipelines

Reusing connections reduces unnecessary overhead and improves overall application performance.

Working with Pandas DataFrames

One of the biggest advantages of clickhouse-connect is its excellent integration with modern Python data tools.

Reading query results directly into a Pandas DataFrame is simple.

df = client.query_df("""
SELECT *
FROM sales
LIMIT 1000
""")
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Likewise, inserting a DataFrame requires only a single function call.

client.insert_df(
    "sales",
    df
)
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These features make clickhouse-connect particularly useful for:

  • Data science
  • Reporting
  • Machine learning
  • Exploratory analytics

Best Practices

Regardless of which client you choose, following a few best practices can significantly improve both performance and maintainability.

  • Reuse client connections whenever possible.
  • Use parameterized queries instead of string formatting.
  • Insert data in batches.
  • Select only the columns you actually need.
  • Perform aggregations inside ClickHouse® rather than Python.
  • Use DataFrame integration for analytical workloads.
  • Monitor slow queries using system.query_log.

Small improvements in client usage often lead to substantial gains in throughput and latency.

Which Client Should You Choose?

If you're starting a new Python project today, clickhouse-connect is generally the recommended option.

Being officially maintained by the ClickHouse® team, it integrates seamlessly with:

  • ClickHouse Cloud
  • Apache Arrow
  • Pandas
  • Modern analytics workflows

At the same time, clickhouse-driver remains an excellent choice for existing applications that already rely on the native TCP protocol.

Many production systems continue to use it successfully.

Ultimately, the best choice depends on your application's architecture, deployment environment, and long-term requirements.

Conclusion

Python and ClickHouse® form a powerful combination for building data-intensive applications, analytics platforms, ETL pipelines, and backend services.

While clickhouse-driver established itself as the original Python client for ClickHouse®, clickhouse-connect represents the modern, officially supported direction of the ecosystem with strong support for cloud deployments, DataFrames, Apache Arrow, and contemporary analytics workflows.

Regardless of which client you choose, following best practices such as reusing connections, executing parameterized queries, batching inserts, and leveraging DataFrame integrations will help you build applications that are both scalable and efficient.

Understanding the strengths of each client allows you to make informed architectural decisions and get the most out of ClickHouse® in your Python projects.

This article is part of my *#100DaysOfClickHouse** series.*

Read more... https://www.quantrail-data.com/using-clickhouse-python-clients-effectively-a-practical-guide

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