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Low level, multiprocessing based AWS Kinesis producer & consumer library

Project description 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.


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.


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


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

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.

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).


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

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.

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.

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