confluent-kafka-python is Confluent's Python client for Apache Kafka and the Confluent Platform.

Features:

• High performance - confluent-kafka-python is a lightweight wrapper around librdkafka, a finely tuned C client.

• Reliability - There are a lot of details to get right when writing an Apache Kafka client. We get them right in one place (librdkafka) and leverage this work across all of our clients (also confluent-kafka-go and confluent-kafka-dotnet).

• Supported - Commercial support is offered by Confluent.

• Future proof - Confluent, founded by the creators of Kafka, is building a streaming platform with Apache Kafka at its core. It's high priority for us that client features keep pace with core Apache Kafka and components of the Confluent Platform.

The Python bindings provides a high-level Producer and Consumer with support for the balanced consumer groups of Apache Kafka 0.9.

See the API documentation for more info.

License: Apache License v2.0

Producer:

from confluent_kafka import Producer

p = Producer({'bootstrap.servers': 'mybroker,mybroker2'})
for data in some_data_source:
    p.produce('mytopic', data.encode('utf-8'))
p.flush()

High-level Consumer:

from confluent_kafka import Consumer, KafkaError

c = Consumer({'bootstrap.servers': 'mybroker', 'group.id': 'mygroup',
              'default.topic.config': {'auto.offset.reset': 'smallest'}})
c.subscribe(['mytopic'])
running = True
while running:
    msg = c.poll()
    if not msg.error():
        print('Received message: %s' % msg.value().decode('utf-8'))
    elif msg.error().code() != KafkaError._PARTITION_EOF:
        print(msg.error())
        running = False
c.close()

AvroProducer

from confluent_kafka import avro
from confluent_kafka.avro import AvroProducer

value_schema = avro.load('ValueSchema.avsc')
key_schema = avro.load('KeySchema.avsc')
value = {"name": "Value"}
key = {"name": "Key"}

avroProducer = AvroProducer({'bootstrap.servers': 'mybroker,mybroker2', 'schema.registry.url': 'http://schem_registry_host:port'}, default_key_schema=key_schema, default_value_schema=value_schema)
avroProducer.produce(topic='my_topic', value=value, key=key)
avroProducer.flush()

AvroConsumer

from confluent_kafka import KafkaError
from confluent_kafka.avro import AvroConsumer
from confluent_kafka.avro.serializer import SerializerError

c = AvroConsumer({'bootstrap.servers': 'mybroker,mybroker2', 'group.id': 'groupid', 'schema.registry.url': 'http://127.0.0.1:8081'})
c.subscribe(['my_topic'])
running = True
while running:
    try:
        msg = c.poll(10)
        if msg:
            if not msg.error():
                print(msg.value())
            elif msg.error().code() != KafkaError._PARTITION_EOF:
                print(msg.error())
                running = False
    except SerializerError as e:
        print("Message deserialization failed for %s: %s" % (msg, e))
        running = False

c.close()

See examples for more examples.

The Python client (as well as the underlying C library librdkafka) supports all broker versions >= 0.8. But due to the nature of the Kafka protocol in broker versions 0.8 and 0.9 it is not safe for a client to assume what protocol version is actually supported by the broker, thus you will need to hint the Python client what protocol version it may use. This is done through two configuration settings:

• broker.version.fallback=YOUR_BROKER_VERSION (default 0.9.0.1)
• api.version.request=true|false (default true)

When using a Kafka 0.10 broker or later you don't need to do anything (api.version.request=true is the default). If you use Kafka broker 0.9 or 0.8 you must set api.version.request=false and set broker.version.fallback to your broker version, e.g broker.version.fallback=0.9.0.1.

More info here: https://github.com/edenhill/librdkafka/wiki/Broker-version-compatibility

• Python >= 2.6 or Python 3.x
• librdkafka >= 0.9.1 (embedded in Linux wheels)

librdkafka is embedded in the manylinux wheels, for other platforms or when a specific version of librdkafka is desired, following these guidelines:

• For Debian/Ubuntu** based systems, add this APT repo and then do sudo apt-get install librdkafka-dev python-dev: http://docs.confluent.io/current/installation.html#installation-apt

• For RedHat and RPM-based distros, add this YUM repo and then do sudo yum install librdkafka-devel python-devel: http://docs.confluent.io/current/installation.html#rpm-packages-via-yum

• On OSX, use homebrew and do sudo brew install librdkafka

Install from PyPi:

$ pip install confluent-kafka

# for AvroProducer or AvroConsumer
$ pip install confluent-kafka[avro]

Install from source / tarball:

$ pip install .

# for AvroProducer or AvroConsumer
$ pip install .[avro]

$ python setup.py build

If librdkafka is installed in a non-standard location provide the include and library directories with:

$ C_INCLUDE_PATH=/path/to/include LIBRARY_PATH=/path/to/lib python setup.py ...

Run unit-tests:

In order to run full test suite, simply execute:

$ tox -r

NOTE: Requires tox (please install with pip install tox), several supported versions of Python on your path, and librdkafka installed into tmp-build.

Run integration tests:

To run the integration tests, uncomment the following line from tox.ini and add the paths to your Kafka and Confluent Schema Registry instances. You can also run the integration tests outside of tox by running this command from the source root.

examples/integration_test.py <kafka-broker> [<test-topic>] [<schema-registry>]

WARNING: These tests require an active Kafka cluster and will create new topics.

Install sphinx and sphinx_rtd_theme packages:

$ pip install sphinx sphinx_rtd_theme

Build HTML docs:

$ make docs

or:

$ python setup.py build_sphinx

Documentation will be generated in docs/_build/.