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Kafka integration with asyncio.

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asyncio client for Kafka

AIOKafkaProducer

AIOKafkaProducer is a high-level, asynchronous message producer.

Example of AIOKafkaProducer usage:

import asyncio
from aiokafka import AIOKafkaProducer

@asyncio.coroutine
def produce(loop):
    # Just adds message to sending queue
    future = yield from producer.send('foobar', b'some_message_bytes')
    # waiting for message to be delivered
    resp = yield from future
    print("Message produced: partition {}; offset {}".format(
          resp.partition, resp.offset))
    # Also can use a helper to send and wait in 1 call
    resp = yield from producer.send_and_wait(
        'foobar', key=b'foo', value=b'bar')
    resp = yield from producer.send_and_wait(
        'foobar', b'message for partition 1', partition=1)

loop = asyncio.get_event_loop()
producer = AIOKafkaProducer(loop=loop, bootstrap_servers='localhost:9092')
# Bootstrap client, will get initial cluster metadata
loop.run_until_complete(producer.start())
loop.run_until_complete(produce(loop))
# Wait for all pending messages to be delivered or expire
loop.run_until_complete(producer.stop())
loop.close()

AIOKafkaConsumer

AIOKafkaConsumer is a high-level, asynchronous message consumer. It interacts with the assigned Kafka Group Coordinator node to allow multiple consumers to load balance consumption of topics (requires kafka >= 0.9.0.0).

Example of AIOKafkaConsumer usage:

import asyncio
from kafka.common import KafkaError
from aiokafka import AIOKafkaConsumer

@asyncio.coroutine
def consume_task(consumer):
    while True:
        try:
            msg = yield from consumer.getone()
            print("consumed: ", msg.topic, msg.partition, msg.offset,
                  msg.key, msg.value, msg.timestamp)
        except KafkaError as err:
            print("error while consuming message: ", err)

loop = asyncio.get_event_loop()
consumer = AIOKafkaConsumer(
    'topic1', 'topic2', loop=loop, bootstrap_servers='localhost:1234')
# Bootstrap client, will get initial cluster metadata
loop.run_until_complete(consumer.start())
c_task = loop.create_task(consume_task(consumer))
try:
    loop.run_forever()
finally:
    # Will gracefully leave consumer group; perform autocommit if enabled
    loop.run_until_complete(consumer.stop())
    c_task.cancel()
    loop.close()

Running tests

Docker is required to run tests. See https://docs.docker.com/engine/installation for installation notes. Also note, that lz4 compression libraries for python will require python-dev package, or python source header files for compilation on Linux.

Setting up tests requirements (assuming you’re within virtualenv on ubuntu 14.04+):

sudo apt-get install -y libsnappy-dev
pip install -r requirements-dev.txt .

Running tests:

make cov

To run tests with a specific version of Kafka (default one is 0.9.0.1) use KAFKA_VERSION variable:

make cov KAFKA_VERSION=0.10.0.0

CHANGES

0.1.4 (2016-11-07)

  • Bumped python-kafka version to 1.3.1 and Kafka to 0.10.1.0.

  • Fixed auto version detection, to correctly handle 0.10.0.0 version

  • Updated Fetch and Produce requests to use v2 with v0.10.0 message format on brokers. This allows a timestamp to be associated with messages.

  • Changed lz4 compression framing, as it was changed due to KIP-57 in new message format.

  • Minor refactorings

Big thanks to @fabregas for the hard work on this release (PR #60)

0.1.3 (2016-10-18)

  • Fixed bug with infinite loop on heartbeats with autocommit=True. #44

  • Bumped python-kafka to version 1.1.1

  • Fixed docker test runner with multiple interfaces

  • Minor documentation fixes

0.1.2 (2016-04-30)

  • Added Python3.5 usage example to docs

  • Don’t raise retriable exceptions in 3.5’s async for iterator

  • Fix Cancellation issue with producer’s send_and_wait method

0.1.1 (2016-04-15)

  • Fix packaging issues. Removed unneded files from package.

0.1.0 (2016-04-15)

Initial release

Added full support for Kafka 9.0. Older Kafka versions are not tested.

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