Metadata-Version: 2.1
Name: kinesis-python
Version: 0.2.1
Summary: Low level, multiprocessing based AWS Kinesis producer & consumer library
Home-page: https://github.com/NerdWalletOSS/kinesis-python
Author: Evan Borgstrom
Author-email: eborgstrom@nerdwallet.com
License: Apache License Version 2.0
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python
Classifier: Topic :: Internet
Classifier: Topic :: Software Development :: Libraries
Requires-Dist: boto3 (<2.0,>=1.4.4)
Requires-Dist: offspring (<1.0,>=0.0.3)
Requires-Dist: six (<2.0,>=1.11.0)

Kinesis Python
==============

.. image:: https://img.shields.io/travis/NerdWalletOSS/kinesis-python.svg
           :target: https://travis-ci.org/NerdWalletOSS/kinesis-python

.. image:: https://img.shields.io/codecov/c/github/NerdWalletOSS/kinesis-python.svg
           :target: https://codecov.io/github/NerdWalletOSS/kinesis-python

.. image:: https://img.shields.io/pypi/v/kinesis-python.svg
           :target: https://pypi.python.org/pypi/kinesis-python
           :alt: Latest PyPI version


The `official Kinesis python library`_ requires the use of Amazon's "MultiLangDaemon", which is a Java executable that
operates by piping messages over STDIN/STDOUT.

.. code-block::

    ಠ_ಠ

While the desire to have a single implementation of the client library from a maintenance standpoint makes sense for
the team responsible for the KPL, requiring the JRE to be installed and having to account for the overhead of the
stream being consumed by Java and Python is not desireable for teams working in environments without Java.

This is a pure-Python implementation of Kinesis producer and consumer classes that leverages Python's multiprocessing
module to spawn a process per shard and then sends the messages back to the main process via a Queue.  It only depends
on `boto3`_ (AWS SDK), `offspring`_ (Subprocess implementation) and `six`_ (py2/py3 compatibility).

It also includes a DynamoDB state back-end that allows for multi-instance consumption of multiple shards, and stores the
checkpoint data so that you can resume where you left off in a stream following restarts or crashes.

.. _boto3: https://pypi.python.org/pypi/boto3
.. _offspring: https://pypi.python.org/pypi/offspring
.. _six: https://pypi.python.org/pypi/six
.. _official Kinesis python library: https://github.com/awslabs/amazon-kinesis-client-python


Overview
--------

All of the functionality is wrapped in two classes: ``KinesisConsumer`` and ``KinesisProducer``

Consumer
~~~~~~~~

The consumer works by launching a process per shard in the stream and then implementing the Python iterator protocol.

.. code-block:: python

    from kinesis.consumer import KinesisConsumer

    consumer = KinesisConsumer(stream_name='my-stream')
    for message in consumer:
        print "Received message: {0}".format(message)

Messages received from each of the shard processes are passed back to the main process through a Python Queue where they
are yielded for processing.  Messages are not strictly ordered, but this is a property of Kinesis and not this
implementation.


Locking, Checkpointing & Multi-instance consumption
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

When deploying an application with multiple instances DynamoDB can be leveraged as a way to coordinate which instance
is responsible for which shard, as it is not desirable to have each instance process all records.

With or without multiple nodes it is also desirable to checkpoint the stream as you process records so that you can
pickup from where you left off if you restart the consumer.

A "state" backend that leverages DynamoDB allows consumers to coordinate which node is responsible which shards and
where in the stream we are currently reading from.

.. code-block:: python

    from kinesis.consumer import KinesisConsumer
    from kinesis.state import DynamoDB

    consumer = KinesisConsumer(stream_name='my-stream', state=DynamoDB(table_name='my-kinesis-state'))
    for message in consumer:
        print "Received message: {0}".format(message)


The DynamoDB table must already exist and must have a ``HASH`` key of ``shard``, with type ``S`` (string).


Producer
~~~~~~~~

The producer works by launching a single process for accumulation and publishing to the stream.

.. code-block:: python

    from kinesis.producer import KinesisProducer

    producer = KinesisProducer(stream_name='my-stream')
    producer.put('Hello World from Python')


By default the accumulation buffer time is 500ms, or the max record size of 1Mb, whichever occurs first.  You can
change the buffer time when you instantiate the producer via the ``buffer_time`` kwarg, specified in seconds.  For
example, if your primary concern is budget and not performance you could accumulate over a 60 second duration.

.. code-block:: python

    producer = KinesisProducer(stream_name='my-stream', buffer_time=60)


The background process takes precaution to ensure that any accumulated messages are flushed to the stream at
shutdown time through signal handlers and the python atexit module, but it is not fully durable and if you were to
send a ``kill -9`` to the producer process any accumulated messages would be lost.



AWS Permissions
---------------

By default the producer, consumer & state classes all use the default `boto3 credentials chain`_.  If you wish to alter
this you can instantiate your own ``boto3.Session`` object and pass it into the constructor via the ``boto3_session``
keyword argument of ``KinesisProducer``, ``KinesisConsumer`` or ``DynamoDB``.

.. _boto3 credentials chain: http://boto3.readthedocs.io/en/latest/guide/configuration.html#configuring-credentials


