PyKafka is a cluster-aware Kafka 0.8.2 protocol client for python. It includes python implementations of Kafka producers and consumers, and runs under python 2.7.

PyKafka’s primary goal is to provide a similar level of abstraction to the JVM Kafka client using idioms familiar to python programmers and exposing the most pythonic API possible.

You can install PyKafka from PyPI with

$ pip install pykafka

Full documentation and usage examples for PyKafka can be found on readthedocs.

You can install PyKafka for local development and testing with

$ python develop

Getting Started

Assuming you have a Kafka instance running on localhost, you can use PyKafka to connect to it.

>>> from pykafka import KafkaClient
>>> client = KafkaClient(hosts="")

If the cluster you’ve connected to has any topics defined on it, you can list them with:

>>> client.topics
{'my.test': <pykafka.topic.Topic at 0x19bc8c0 (name=my.test)>}
>>> topic = client.topics['my.test']

Once you’ve got a Topic, you can create a Producer for it and start producing messages.

>>> with topic.get_producer() as producer:
...     for i in range(4):
...         producer.produce('test message ' + i ** 2)

You can also consume messages from this topic using a Consumer instance.

>>> consumer = topic.get_simple_consumer()
>>> for message in consumer:
...     if message is not None:
...         print message.offset, message.value
0 test message 0
1 test message 1
2 test message 4
3 test message 9

This SimpleConsumer doesn’t scale - if you have two SimpleConsumers consuming the same topic, they will receive duplicate messages. To get around this, you can use the BalancedConsumer.

>>> balanced_consumer = topic.get_balanced_consumer(
...     consumer_group='testgroup',
...     auto_commit_enable=True,
...     zookeeper_connect=','
... )

You can have as many BalancedConsumer instances consuming a topic as that topic has partitions. If they are all connected to the same zookeeper instance, they will communicate with it to automatically balance the partitions between themselves.

Operational Tools

PyKafka includes a small collection of CLI tools that can help with common tasks related to the administration of a Kafka cluster, including offset and lag monitoring and topic inspection. The full, up-to-date interface for these tools can be fould by running

or after installing PyKafka via setuptools or pip:

What happened to Samsa?

This project used to be called samsa. It has been renamed PyKafka and has been fully overhauled to support Kafka 0.8.2. We chose to target 0.8.2 because the offset Commit/Fetch API is stabilized.

The Samsa PyPI package will stay up for the foreseeable future and tags for previous versions will always be available in this repo.