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https://github.com/Parsely/pykafka

Apache Kafka client for Python; high-level & low-level consumer/producer, with great performance.
https://github.com/Parsely/pykafka

apache-kafka c-extension kafka python

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Apache Kafka client for Python; high-level & low-level consumer/producer, with great performance.

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PyKafka

=======



.. image:: http://i.imgur.com/ztYl4lG.jpg



PyKafka is a programmer-friendly Kafka client for Python. It includes Python

implementations of Kafka producers and consumers, which are optionally backed

by a C extension built on `librdkafka`_. It runs under Python 2.7+, Python 3.4+,

and PyPy, and supports versions of Kafka 0.8.2 and newer.



.. _librdkafka: https://github.com/edenhill/librdkafka



PyKafka's primary goal is to provide a similar level of abstraction to the

`JVM Kafka client`_ using idioms familiar to Python programmers and exposing

the most Pythonic API possible.



You can install PyKafka from PyPI with



::



    $ pip install pykafka



or from conda-forge with



::



    $ conda install -c conda-forge pykafka



Full documentation and usage examples for PyKafka can be found on `readthedocs`_.



You can install PyKafka for local development and testing by cloning this repository and

running



::



    $ python setup.py develop



.. _JVM Kafka client: https://github.com/apache/kafka/tree/0.8.2/clients/src/main/java/org/apache/kafka

.. _readthedocs: http://pykafka.readthedocs.org/en/latest/



Getting Started

---------------



Assuming you have at least one Kafka instance running on localhost, you can use PyKafka

to connect to it.



.. sourcecode:: python



    >>> from pykafka import KafkaClient

    >>> client = KafkaClient(hosts="127.0.0.1:9092,127.0.0.1:9093,...")



Or, for a TLS connection, you might write (and also see ``SslConfig`` docs

for further details):



.. sourcecode:: python



    >>> from pykafka import KafkaClient, SslConfig

    >>> config = SslConfig(cafile='/your/ca.cert',

    ...                    certfile='/your/client.cert',  # optional

    ...                    keyfile='/your/client.key',  # optional

    ...                    password='unlock my client key please')  # optional

    >>> client = KafkaClient(hosts="127.0.0.1:,...",

    ...                      ssl_config=config)



If the cluster you've connected to has any topics defined on it, you can list

them with:



.. sourcecode:: python



    >>> client.topics

    >>> topic = client.topics['my.test']



Once you've got a `Topic`, you can create a `Producer` for it and start

producing messages.



.. sourcecode:: python



    >>> with topic.get_sync_producer() as producer:

    ...     for i in range(4):

    ...         producer.produce('test message ' + str(i ** 2))



The example above would produce to kafka synchronously - the call only

returns after we have confirmation that the message made it to the cluster.



To achieve higher throughput, we recommend using the ``Producer`` in

asynchronous mode, so that ``produce()`` calls will return immediately and the

producer may opt to send messages in larger batches. The ``Producer`` collects

produced messages in an internal queue for ``linger_ms`` before sending each batch.

This delay can be removed or changed at the expense of efficiency with ``linger_ms``,

``min_queued_messages``, and other keyword arguments (see `readthedocs`_). You can still obtain

delivery confirmation for messages, through a queue interface which can be

enabled by setting ``delivery_reports=True``.  Here's a rough usage example:



.. sourcecode:: python



    >>> with topic.get_producer(delivery_reports=True) as producer:

    ...     count = 0

    ...     while True:

    ...         count += 1

    ...         producer.produce('test msg', partition_key='{}'.format(count))

    ...         if count % 10 ** 5 == 0:  # adjust this or bring lots of RAM ;)

    ...             while True:

    ...                 try:

    ...                     msg, exc = producer.get_delivery_report(block=False)

    ...                     if exc is not None:

    ...                         print 'Failed to deliver msg {}: {}'.format(

    ...                             msg.partition_key, repr(exc))

    ...                     else:

    ...                         print 'Successfully delivered msg {}'.format(

    ...                         msg.partition_key)

    ...                 except Queue.Empty:

    ...                     break



Note that the delivery report queue is thread-local: it will only serve reports

for messages which were produced from the current thread. Also, if you're using

`delivery_reports=True`, failing to consume the delivery report queue will cause

PyKafka's memory usage to grow unbounded.



You can also consume messages from this topic using a `Consumer` instance.



.. sourcecode:: python



    >>> consumer = topic.get_simple_consumer()

    >>> for message in consumer:

    ...     if message is not None:

    ...         print message.offset, message.value

    0 test message 0

    1 test message 1

    2 test message 4

    3 test message 9



This `SimpleConsumer` doesn't scale - if you have two `SimpleConsumers`

consuming the same topic, they will receive duplicate messages. To get around

this, you can use the `BalancedConsumer`.



.. sourcecode:: python



    >>> balanced_consumer = topic.get_balanced_consumer(

    ...     consumer_group='testgroup',

    ...     auto_commit_enable=True,

    ...     zookeeper_connect='myZkClusterNode1.com:2181,myZkClusterNode2.com:2181/myZkChroot'

    ... )



You can have as many `BalancedConsumer` instances consuming a topic as that

topic has partitions. If they are all connected to the same zookeeper instance,

they will communicate with it to automatically balance the partitions between

themselves. The partition assignment strategy used by the `BalancedConsumer` is

the "range" strategy by default. The strategy is switchable via the `membership_protocol`

keyword argument, and can be either an object exposed by `pykafka.membershipprotocol` or

a custom instance of `pykafka.membershipprotocol.GroupMembershipProtocol`.



You can also use the Kafka 0.9 Group Membership API with the ``managed``

keyword argument on ``get_balanced_consumer``.



Using the librdkafka extension

------------------------------



PyKafka includes a C extension that makes use of librdkafka to speed up producer

and consumer operation.



To ensure the C extension is compiled, set environment variable ``RDKAFKA_INSTALL=system`` during

``pip install`` or ``setup.py``, i.e. ``RDKAFKA_INSTALL=system pip install pykafka``.

The setup will fail if C extension is not compiled. Oppositely, if ``RDKAFKA_INSTALL=''``, this

explicitly specifies that the C extension should not be compiled. The current default behavior is

to compile the extension but will not fail the setup if compilation fails.



PyKafka requires librdkafka v0.9.1+. Some system package managers may not have

up-to-date versions. To use the librdkafka extension, you need to make sure the header

files and shared library are somewhere where python can find them, both when you build

the extension (which is taken care of by ``setup.py develop``) and at run time.

Typically, this means that you need to either install librdkafka in a place

conventional for your system, or declare ``C_INCLUDE_PATH``, ``LIBRARY_PATH``,

and ``LD_LIBRARY_PATH`` in your shell environment to point to the installation location

of the librdkafka shared objects. You can find this location with `locate librdkafka.so`.



After that, all that's needed is that you pass an extra parameter

``use_rdkafka=True`` to ``topic.get_producer()``,

``topic.get_simple_consumer()``, or ``topic.get_balanced_consumer()``.  Note

that some configuration options may have different optimal values; it may be

worthwhile to consult librdkafka's `configuration notes`_ for this.



.. _0.9.1: https://github.com/edenhill/librdkafka/releases/tag/0.9.1

.. _configuration notes: https://github.com/edenhill/librdkafka/blob/0.9.1/CONFIGURATION.md



Operational Tools

-----------------



PyKafka includes a small collection of `CLI tools`_ that can help with common tasks

related to the administration of a Kafka cluster, including offset and lag monitoring and

topic inspection. The full, up-to-date interface for these tools can be found by running



.. sourcecode:: sh



    $ python cli/kafka_tools.py --help



or after installing PyKafka via setuptools or pip:



.. sourcecode:: sh



    $ kafka-tools --help



.. _CLI tools: https://github.com/Parsely/pykafka/blob/master/pykafka/cli/kafka_tools.py



PyKafka or kafka-python?

------------------------



These are two different projects.

See `the discussion here `_ for comparisons

between the two projects.



Contributing

------------



If you're interested in contributing code to PyKafka, a good place to start is the

`"help wanted"`_ issue tag. We also recommend taking a look at the `contribution guide`_.



.. _"help wanted": https://github.com/Parsely/pykafka/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22



Support

-------



If you need help using PyKafka, there are a bunch of resources available.

For usage questions or common recipes, check out the `StackOverflow tag`_.

The `Google Group`_ can be useful for more in-depth questions or inquiries

you'd like to send directly to the PyKafka maintainers. If you believe you've

found a bug in PyKafka, please open a `github issue`_ after reading the

`contribution guide`_.



.. _StackOverflow tag: https://stackoverflow.com/questions/tagged/pykafka

.. _github issue: https://github.com/Parsely/pykafka/issues

.. _Google Group: https://groups.google.com/forum/#!forum/pykafka-user

.. _contribution guide: https://github.com/Parsely/pykafka/blob/master/CONTRIBUTING.rst