testbed/dpkp__kafka-python/docs/usage.rst

Usage
*****


KafkaConsumer
=============

.. code:: python

    from kafka import KafkaConsumer

    # To consume latest messages and auto-commit offsets
    consumer = KafkaConsumer('my-topic',
                             group_id='my-group',
                             bootstrap_servers=['localhost:9092'])
    for message in consumer:
        # message value and key are raw bytes -- decode if necessary!
        # e.g., for unicode: `message.value.decode('utf-8')`
        print ("%s:%d:%d: key=%s value=%s" % (message.topic, message.partition,
                                              message.offset, message.key,
                                              message.value))

    # consume earliest available messages, don't commit offsets
    KafkaConsumer(auto_offset_reset='earliest', enable_auto_commit=False)

    # consume json messages
    KafkaConsumer(value_deserializer=lambda m: json.loads(m.decode('ascii')))

    # consume msgpack 
    KafkaConsumer(value_deserializer=msgpack.unpackb)

    # StopIteration if no message after 1sec
    KafkaConsumer(consumer_timeout_ms=1000)

    # Subscribe to a regex topic pattern
    consumer = KafkaConsumer()
    consumer.subscribe(pattern='^awesome.*')

    # Use multiple consumers in parallel w/ 0.9 kafka brokers
    # typically you would run each on a different server / process / CPU
    consumer1 = KafkaConsumer('my-topic',
                              group_id='my-group',
                              bootstrap_servers='my.server.com')
    consumer2 = KafkaConsumer('my-topic',
                              group_id='my-group',
                              bootstrap_servers='my.server.com')


There are many configuration options for the consumer class. See
:class:`~kafka.KafkaConsumer` API documentation for more details.


KafkaProducer
==============

.. code:: python

    from kafka import KafkaProducer
    from kafka.errors import KafkaError

    producer = KafkaProducer(bootstrap_servers=['broker1:1234'])

    # Asynchronous by default
    future = producer.send('my-topic', b'raw_bytes')

    # Block for 'synchronous' sends
    try:
        record_metadata = future.get(timeout=10)
    except KafkaError:
        # Decide what to do if produce request failed...
        log.exception()
        pass

    # Successful result returns assigned partition and offset
    print (record_metadata.topic)
    print (record_metadata.partition)
    print (record_metadata.offset)

    # produce keyed messages to enable hashed partitioning
    producer.send('my-topic', key=b'foo', value=b'bar')

    # encode objects via msgpack
    producer = KafkaProducer(value_serializer=msgpack.dumps)
    producer.send('msgpack-topic', {'key': 'value'})

    # produce json messages
    producer = KafkaProducer(value_serializer=lambda m: json.dumps(m).encode('ascii'))
    producer.send('json-topic', {'key': 'value'})

    # produce asynchronously
    for _ in range(100):
        producer.send('my-topic', b'msg')

    def on_send_success(record_metadata):
        print(record_metadata.topic)
        print(record_metadata.partition)
        print(record_metadata.offset)

    def on_send_error(excp):
        log.error('I am an errback', exc_info=excp)
        # handle exception

    # produce asynchronously with callbacks 
    producer.send('my-topic', b'raw_bytes').add_callback(on_send_success).add_errback(on_send_error)

    # block until all async messages are sent
    producer.flush()

    # configure multiple retries
    producer = KafkaProducer(retries=5)