Choosing the Best Kafka Client for Python: A Practical Guide

When building robust, high-throughput data pipelines in Python, selecting the right Kafka client is crucial. In this post, I’ll share my small analysis of the most popular Kafka clients for Python, highlight their strengths and weaknesses, and explain why I recommend confluent-kafka-python for most production use cases.

Motivation

I initially chose kafka-python due to its popularity on GitHub and recommendations from the first articles in google search. However, over time, I encountered several issues that prompted me to search for a more stable and feature-rich alternative.

Problems with kafka-python:

• Inconsistent development: Long gaps between releases and buggy updates.
• Bugs: After updating the library, I experienced issues with my producer. It took a month for a new version with fixes to be released.
• Limited features: The library lacks support for many advanced Kafka features available in the Java client.

Goal:
Find a reliable, performant, and future-proof Kafka client for Python that supports modern Kafka features (idempotence, high throughput, transactions, etc.).

Candidate Kafka Clients for Python

After reviewing top articles, GitHub repositories, and company usage, I shortlisted the following Kafka clients. Deprecated or unmaintained libraries were excluded.

A. Confluent Kafka Python (confluent-kafka-python)

• Core: Thin wrapper over librdkafka (C/C++), the de facto standard for Kafka clients outside Java.
• Features:
  • High performance (on par with the Java client for large messages).
  • Actively maintained by Confluent (founded by the original creators of Apache Kafka).
  • Supports advanced Kafka features: idempotence, transactions, schema registry, etc.
  • Good documentation and commercial support.
  • Thread-safe and can handle high-throughput workloads.
• Drawbacks:
  • Written in C/C++. While this offers performance benefits, it means you likely won’t modify the library directly.
  • More dependencies (requires a C library, but wheels are provided for most platforms).

B. aiokafka

• Core: Mostly pure Python, built for asyncio.

• Features:

  • Asynchronous API.
  • Good for moderate workloads.
  • Maintained, though not as actively as Confluent.

  • Previously used kafka-python under the hood, but recent versions do not.

    kafka-python is not actively maintained or getting any of the new features (deprecated) dpkp/kafka-python#2290. We should help the developers of aiokafka towards the standalone package goal as discussed here #915

• Drawbacks:

  • Lower performance than C-backed clients.
  • Feature set lags behind Confluent/librdkafka.

Fun fact: In the code of aiokafka, you can find some copied code from librdkafka (confluent-kafka-python is a wrapper for it) and kafka-python.

C. Quix Streams

• Core: High-level, DataFrame-like API, wraps the Confluent client.
• Features:
  • Easiest for data scientists (pandas-like).
• Drawbacks:
  • Adds abstraction, which may not suit low-level tuning or minimal dependencies. It is often better to use confluent-kafka directly for more control for production.

Comparative Table

Feature	kafka-python	confluent-kafka-python	aiokafka	Quix Streams
Language core	Pure Python	C (librdkafka) + Python	Pure Python	Python (wraps Confluent)
Performance	Moderate	High (near Java client)	Moderate	High (via Confluent)
Advanced Kafka features	Partial	Full	Partial	Full (via Confluent)
Maintenance	Inconsistent	Active, commercial	Active	Active
Documentation	Sparse	Good	Good	Good
Use case fit	Simple projects	Production, high-load	Async apps	Data science

Primary Recommendation: confluent-kafka-python

Why?

• Performance:
  C-backed, matches the Java client for throughput and latency.
• Reliability:
  Used in production by many companies, maintained by Kafka creators. The core library, librdkafka, is used (wholly or in part) in all candidates.
• Feature-complete:
  Supports advanced features: transactions, schema registry, etc.
• Active development:
  Frequent releases, quick bug fixes, and responsiveness to Kafka protocol changes.

Conclusion

For most production use cases, especially those requiring high throughput, reliability, and advanced Kafka features, confluent-kafka-python is the best choice in my opinion. It combines performance, active maintenance, and feature completeness, making it a future-proof option for Python-based Kafka pipelines.