Amazon Kinesis Client Library for Python
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amazon_kclpy
samples
LICENSE.txt
MANIFEST.in
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README.md
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README.md

Amazon Kinesis Client Library for Python

This package provides an interface to the Amazon Kinesis Client Library (KCL) MultiLangDaemon, which is part of the Amazon KCL for Java. Developers can use the Amazon KCL to build distributed applications that process streaming data reliably at scale. The Amazon KCL takes care of many of the complex tasks associated with distributed computing, such as load-balancing across multiple instances, responding to instance failures, checkpointing processed records, and reacting to changes in stream volume. This interface manages the interaction with the MultiLangDaemon so that developers can focus on implementing their record processor executable. A record processor executable typically looks something like:

    #!env python
    from amazon_kclpy import kcl
    import json, base64

    class RecordProcessor(kcl.RecordProcessorBase):

        def initialize(self, shard_id):
            pass

        def process_records(self, records, checkpointer):
            pass

        def shutdown(self, checkpointer, reason):
            pass

    if __name__ == "__main__":
        kclprocess = kcl.KCLProcess(RecordProcessor())
        kclprocess.run()

Before You Get Started

Before running the samples, you'll want to make sure that your environment is configured to allow the samples to use your AWS Security Credentials.

By default the samples use the DefaultAWSCredentialsProviderChain so you'll want to make your credentials available to one of the credentials providers in that provider chain. There are several ways to do this such as providing a ~/.aws/credentials file, or if you're running on EC2, you can associate an IAM role with your instance with appropriate access.

For questions regarding Amazon Kinesis Service and the client libraries please visit the Amazon Kinesis Forums

Running the Sample

Using the amazon_kclpy package requires the MultiLangDaemon which is provided by the Amazon KCL for Java. These jars will be downloaded automatically by the install command, but you can explicitly download them with the download_jars command. From the root of this repo, run:

python setup.py download_jars
python setup.py install

Now the amazon_kclpy and boto (used by the sample putter script) and required jars should be installed in your environment. To start the sample putter, run:

sample_kinesis_wordputter.py --stream words -w cat -w dog -w bird -w lobster

This will create an Amazon Kinesis stream called words and put the words specified by the -w options into the stream once each. Use -p SECONDS to indicate a period over which to repeatedly put these words.

Now we would like to run an Amazon KCL for Python application that reads records from the stream we just created, but first take a look in the samples directory, you'll find a file called sample.properties, cat that file:

cat samples/sample.properties

You'll see several properties defined there. executableName indicates the executable for the MultiLangDaemon to run, streamName is the Kinesis stream to read from, appName is the Amazon KCL application name to use which will be the name of an Amazon DynamoDB table that gets created by the Amazon KCL, initialPositionInStream tells the Amazon KCL how to start reading from shards upon a fresh startup. To run the sample application you can use a helper script included in this package. Note you must provide a path to java (version 1.7 or greater) to run the Amazon KCL.

amazon_kclpy_helper.py --print_command \
    --java <path-to-java> --properties samples/sample.properties

This will print the command needed to run the sample which you can copy paste, or surround the command with back ticks to run it.

`amazon_kclpy_helper.py --print_command \
    --java <path-to-java> --properties samples/sample.properties`

Alternatively, if you don't have the source on hand, but want to run the sample app you can use the --sample argument to indicate you'd like to get the sample.properties file from the installation location.

amazon_kclpy_helper.py --print_command --java <path-to-java> --sample

Running on EC2

Running on EC2 is simple. Assuming you are already logged into an EC2 instance running Amazon Linux, the following steps will prepare your environment for running the sample app. Note the version of java that ships with Amazon Linux can be found at /usr/bin/java and should be 1.7 or greater.

sudo yum install python-pip

sudo pip install virtualenv

virtualenv /tmp/kclpy-sample-env

source /tmp/kclpy-sample-env/bin/activate

pip install amazon_kclpy

Under the Hood - What You Should Know about Amazon KCL's MultiLangDaemon

Amazon KCL for Python uses Amazon KCL for Java internally. We have implemented a Java-based daemon, called the MultiLangDaemon that does all the heavy lifting. Our approach has the daemon spawn the user-defined record processor script/program as a sub-process. The MultiLangDaemon communicates with this sub-process over standard input/output using a simple protocol, and therefore the record processor script/program can be written in any language.

At runtime, there will always be a one-to-one correspondence between a record processor, a child process, and an Amazon Kinesis Shard. The MultiLangDaemon will make sure of that, without any need for the developer to intervene.

In this release, we have abstracted these implementation details away and exposed an interface that enables you to focus on writing record processing logic in Python. This approach enables Amazon KCL to be language agnostic, while providing identical features and similar parallel processing model across all languages.

See Also

Release Notes

Release 1.2.0

  • Updated dependency to Amazon KCL version 1.6.1

Release 1.1.0 (January 27, 2015)

  • Python 3 support All Python files are compatible with Python 3

Release 1.0.0 (October 21, 2014)

  • amazon_kclpy module exposes an interface to allow implementation of record processor executables that are compatible with the MultiLangDaemon
  • samples module provides a sample putter application using boto and a sample processing app using amazon_kclpy