Kafka Python client

DUE TO ISSUES WITH RELEASES, IT IS SUGGESTED TO USE https://github.com/wbarnha/kafka-python-ng FOR THE TIME BEING

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 (0.9+), but is backwards-compatible with older versions (to 0.8.0). Some features will only be enabled on newer brokers. 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 <https://kafka-python.readthedocs.io/en/master/compatibility.html> 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

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 <https://kafka-python.readthedocs.io/en/master/apidoc/KafkaConsumer.html> 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)

>>> # Access record headers. The returned value is a list of tuples
>>> # with str, bytes for key and value
>>> for msg in consumer:
...     print (msg.headers)

>>> # Get consumer metrics
>>> metrics = consumer.metrics()

KafkaProducer

KafkaProducer is a high-level, asynchronous message producer. The class is intended to operate as similarly as possible to the official java client. See <https://kafka-python.readthedocs.io/en/master/apidoc/KafkaProducer.html> 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
>>> producer.flush()

>>> # 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)

>>> # Include record headers. The format is list of tuples with string key
>>> # and bytes value.
>>> producer.send('foobar', value=b'c29tZSB2YWx1ZQ==', headers=[('content-encoding', b'base64')])

>>> # Get producer performance metrics
>>> metrics = producer.metrics()

Thread safety

The KafkaProducer can be used across threads without issue, unlike the KafkaConsumer which cannot.

While it is possible to use the KafkaConsumer in a thread-local manner, multiprocessing is recommended.

Compression

kafka-python supports the following compression formats:

• gzip
• LZ4
• Snappy
• Zstandard (zstd)

gzip is supported natively, the others require installing additional libraries. See <https://kafka-python.readthedocs.io/en/master/install.html> for more information.

Optimized CRC32 Validation

Kafka uses CRC32 checksums to validate messages. kafka-python includes a pure python implementation for compatibility. To improve performance for high-throughput applications, kafka-python will use crc32c for optimized native code if installed. See <https://kafka-python.readthedocs.io/en/master/install.html> for installation instructions. See https://pypi.org/project/crc32c/ for details on the underlying crc32c lib.

Protocol

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 2.6+).