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Python client for Apache Kafka

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Statistics on kafka-python

Number of watchers on Github 12
Number of open issues 4
Main language Python
Average time to merge a PR 5 days
Open pull requests 5+
Closed pull requests 6+
Last commit about 2 years ago
Repo Created over 4 years ago
Repo Last Updated over 2 years ago
Size 3.4 MB
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Kafka Python client

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Python client for the Apache Kafka distributed stream processing system. kafka-python is designed to function much like the official java client, with a sprinkling of pythonic interfaces (e.g., consumer iterators).

kafka-python is best used with newer brokers (0.10 or 0.9), but is backwards-compatible with older versions (to 0.8.0). Some features will only be enabled on newer brokers, however; for example, fully coordinated consumer groups -- i.e., dynamic partition assignment to multiple consumers in the same group -- requires use of 0.9+ kafka brokers. Supporting this feature for earlier broker releases would require writing and maintaining custom leadership election and membership / health check code (perhaps using zookeeper or consul). For older brokers, you can achieve something similar by manually assigning different partitions to each consumer instance with config management tools like chef, ansible, etc. This approach will work fine, though it does not support rebalancing on failures. See for more details.

Please note that the master branch may contain unreleased features. For release documentation, please see readthedocs and/or python's inline help.

pip install kafka-python


KafkaConsumer is a high-level message consumer, intended to operate as similarly as possible to the official java client. Full support for coordinated consumer groups requires use of kafka brokers that support the Group APIs: kafka v0.9+.

See for API and configuration details.

The consumer iterator returns ConsumerRecords, which are simple namedtuples that expose basic message attributes: topic, partition, offset, key, and value:

from kafka import KafkaConsumer consumer = KafkaConsumer('my_favorite_topic') for msg in consumer: ... print (msg)

join a consumer group for dynamic partition assignment and offset commits

from kafka import KafkaConsumer consumer = KafkaConsumer('my_favorite_topic', group_id='my_favorite_group') for msg in consumer: ... print (msg)

manually assign the partition list for the consumer

from kafka import TopicPartition consumer = KafkaConsumer(bootstrap_servers='localhost:1234') consumer.assign([TopicPartition('foobar', 2)]) msg = next(consumer)

Deserialize msgpack-encoded values

consumer = KafkaConsumer(value_deserializer=msgpack.loads) consumer.subscribe(['msgpackfoo']) for msg in consumer: ... assert isinstance(msg.value, dict)


KafkaProducer is a high-level, asynchronous message producer. The class is intended to operate as similarly as possible to the official java client. See for more details.

from kafka import KafkaProducer producer = KafkaProducer(bootstrap_servers='localhost:1234') for _ in range(100): ... producer.send('foobar', b'some_message_bytes')

Block until a single message is sent (or timeout)

future = producer.send('foobar', b'another_message') result = future.get(timeout=60)

Block until all pending messages are at least put on the network

NOTE: This does not guarantee delivery or success! It is really

only useful if you configure internal batching using linger_ms


Use a key for hashed-partitioning

producer.send('foobar', key=b'foo', value=b'bar')

Serialize json messages

import json producer = KafkaProducer(value_serializer=lambda v: json.dumps(v).encode('utf-8')) producer.send('fizzbuzz', {'foo': 'bar'})

Serialize string keys

producer = KafkaProducer(key_serializer=str.encode) producer.send('flipflap', key='ping', value=b'1234')

Compress messages

producer = KafkaProducer(compression_type='gzip') for i in range(1000): ... producer.send('foobar', b'msg %d' % i)


kafka-python supports gzip compression/decompression natively. To produce or consume lz4 compressed messages, you must install lz4tools and xxhash (modules may not work on python2.6). To enable snappy compression/decompression install python-snappy (also requires snappy library). See for more information.


A secondary goal of kafka-python is to provide an easy-to-use protocol layer for interacting with kafka brokers via the python repl. This is useful for testing, probing, and general experimentation. The protocol support is leveraged to enable a KafkaClient.check_version() method that probes a kafka broker and attempts to identify which version it is running (0.8.0 to 0.10).


Legacy support is maintained for low-level consumer and producer classes, SimpleConsumer and SimpleProducer. See for API details.

kafka-python open pull requests (View All Pulls)
  • Upgrade to v1.3.4 (Test branch)
  • Upgrade to v1.3.4 : Part-1
  • Upgrade to 1.3.4
  • Prevent Fetcher from wrongfully discarding PartitionRecords in compacted topics
  • Upgrade to 1.4
kafka-python questions on Stackoverflow (View All Questions)
  • Kafka Error INVALID_ARG No Such configuration property sasl.mechanisms when using confluent-kafka-python
  • Connecting Kafka-Python with a cluster with Kerberos
  • How to connect to Message Hub from Data Science Experience / Spark as a Service using confluent-kafka-python?
  • Error on Installing Apache kafka python client
  • kafka-python error getting request key
  • kafka-python - How do I commit a partition?
  • Use kafka-python, KafkaProducer.send timeout
  • kafka-python: consume in precedence from priority topics
  • kafka-python: producer is not able to connect
  • Sending data with kafka-python only working when briefly delaying code
  • How to subscribe to a list of multiple kafka wildcard patterns using kafka-python?
  • kafka-python consumer giving errors
  • How to make kafka-python or pykafka work as an async producer with uwsgi and gevent?
  • Why kafka-python fails to connect to Bluemix message hub service?
  • confluent-kafka python avro messages
  • multiprocessing in kafka-python
  • kafka-python raise kafka.errors.ConsumerFetchSizeTooSmall
  • kafka-python: Features in the pipeline
  • Kafka-python retrieve the list of topics
  • kafka-python consumer not receiving messages
  • Kafka python consumer reading all the messages when started
  • Reading oldest available message in Kafka using KafkaConsumer instance of kafka-python kafka client
  • consuming message in client in kafka-python
  • Restarting a Kafka (python) consumer consumes all the messages in the queue again
  • Kafka-python get number of partitions for topic
  • Restarting a Kafka python MultiProcessConsumer consumes all the messages in the queue again
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