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The Amazon Kinesis Client Library (KCL) for Java enables Java developers to easily consume and process data from Amazon Kinesis Data Streams.

• Kinesis Data Streams Product Page
• Amazon re:Post Forum: Kinesis
• Javadoc
• FAQ
• Developer Guide - Kinesis Client Library
• KCL GitHub documentation (folder)
• Issues
• Giving Feedback

• Scalability: KCL enables applications to scale dynamically by distributing the processing load across multiple workers. You can scale your application in or out, manually or with auto-scaling, without worrying about load redistribution.
• Load balancing: KCL automatically balances the processing load across available workers, resulting in an even distribution of work across workers.
• Checkpointing: KCL manages checkpointing of processed records, enabling applications to resume processing from their last sucessfully processed position.
• Fault tolerance: KCL provides built-in fault tolerance mechanisms, making sure that data processing continues even if individual workers fail. KCL also provides at-least-once delivery.
• Handling stream-level changes: KCL adapts to shard splits and merges that might occur due to changes in data volume. It maintains ordering by making sure that child shards are processed only after their parent shard is completed and checkpointed.
• Monitoring: KCL integrates with Amazon CloudWatch for consumer-level monitoring.
• Multi-language support: KCL natively supports Java and enables multiple non-Java programming languages through MultiLangDaemon.

1. Sign up for AWS — Before you begin, you need an AWS account. For more information about creating an AWS account and retrieving your AWS credentials, see AWS Account and Credentials in the AWS SDK for Java Developer Guide.
2. Sign up for Amazon Kinesis — Go to the Amazon Kinesis console to sign up for the service and create an Amazon Kinesis stream. For more information, see Create an Amazon Kinesis Stream in the Amazon Kinesis Developer Guide.
3. Minimum requirements — To use the Amazon Kinesis Client Library, you will need Java 1.8+. For more information about Amazon Kinesis Client Library requirements, see Before You Begin in the Amazon Kinesis Developer Guide.
4. Using the Amazon Kinesis Client Library — The best way to get familiar with the Amazon Kinesis Client Library is to read Use Kinesis Client Library in the Amazon Kinesis Data Streams Developer Guide. For more information on core KCL concepts, please refer to the [KCL Concepts][kinesis-client-library-concepts] page.

After you have downloaded the code from GitHub, you can build it using Maven. To disable GPG signing in the build, use this command: mvn clean install -Dgpg.skip=true. Note: This command does not run integration tests.

To disable running unit tests in the build, add the property -DskipUTs=true.

Note that running integration tests creates AWS resources. Integration tests require valid AWS credentials. This will look for a default AWS profile specified in your local .aws/credentials. To run all integration tests: mvn verify -DskipITs=false. To run one integration tests, specify the integration test class: mvn -Dit.test="BasicStreamConsumerIntegrationTest" -DskipITs=false verify Optionally, you can provide the name of an IAM user/role to run tests with as a string using this command: mvn -DskipITs=false -DawsProfile="<PROFILE_NAME>" verify.

For producer-side developers using the Kinesis Producer Library (KPL), the KCL integrates without additional effort. When the KCL retrieves an aggregated Amazon Kinesis record consisting of multiple KPL user records, it will automatically invoke the KPL to extract the individual user records before returning them to the user.

To make it easier for developers to write record processors in other languages, we have implemented a Java based daemon, called MultiLangDaemon that does all the heavy lifting. Our approach has the daemon spawn a sub-process, which in turn runs the record processor, which can be written in any language. The MultiLangDaemon process and the record processor sub-process communicate with each other over STDIN and STDOUT using a defined protocol. There will be a one to one correspondence amongst record processors, child processes, and shards. For Python developers specifically, we have abstracted these implementation details away and expose an interface that enables you to focus on writing record processing logic in Python. This approach enables KCL to be language agnostic, while providing identical features and similar parallel processing model across all languages.

The recommended way to use the KCL for Java is to consume it from Maven.

<dependency>
    <groupId>software.amazon.kinesis</groupId>
    <artifactId>amazon-kinesis-client</artifactId>
    <version>3.5.4</version>
</dependency>

Version 2.x tracking branch

<dependency>
    <groupId>software.amazon.kinesis</groupId>
    <artifactId>amazon-kinesis-client</artifactId>
    <version>2.7.0</version>
</dependency>

Version 1.x tracking branch

<dependency>
    <groupId>com.amazonaws</groupId>
    <artifactId>amazon-kinesis-client</artifactId>
    <version>1.14.1</version>
</dependency>

We recommend all users to migrate to the latest respective versions to avoid known issues and benefit from all improvements.

Help Us Improve the Kinesis Client Library! Your involvement is crucial to enhancing the Kinesis Client Library. We invite you to join our community and contribute in the following ways:

• Issue Reporting: This is our preferred method of communication. Use this channel to report bugs, suggest improvements, or ask questions.
• Feature Requests: Share your ideas for new features or vote for existing proposals on our Issues page. This helps us prioritize development efforts.
• Participate in Discussions: Engage with other users and our team in our discussion forums.
• Submit Pull Requests: If you have developed a fix or improvement, we welcome your code contributions.

By participating through these channels, you play a vital role in shaping the future of the Kinesis Client Library. We value your input and look forward to collaborating with you!