Kinesis Python

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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