Client library for Amazon Kinesis
Find a file
2025-03-12 10:29:02 -07:00
.github Remove SDK v1 from KCL v2 (#1444) 2025-03-10 16:20:38 -07:00
amazon-kinesis-client Prepare for release v2.7.0 (#1445) 2025-03-11 11:17:36 -07:00
amazon-kinesis-client-multilang Prepare for release v2.7.0 (#1445) 2025-03-11 11:17:36 -07:00
checkstyle Fix checkstyle violations. 2024-05-21 16:35:31 -07:00
docs Update documentation for upgrading to KCL v2.7.x (#1446) 2025-03-12 10:29:02 -07:00
formatter Release 2.0.0 of the Amazon Kinesis Client for Java 2018-08-02 12:19:46 -07:00
META-INF Release 2.0.0 of the Amazon Kinesis Client for Java 2018-08-02 12:19:46 -07:00
.git-blame-ignore-revs ignore format commit in blame 2024-05-21 01:27:20 -07:00
.gitignore Removed a .swp file, and updated .gitignore. (#1043) 2023-02-27 10:02:36 -08:00
.travis.yml Advance version, and drop Java 7 support (#176) 2017-07-20 12:03:34 -07:00
CHANGELOG.md Prepare for release v2.7.0 (#1445) 2025-03-11 11:17:36 -07:00
CODE_OF_CONDUCT.md Adding standard files (#302) 2018-02-26 10:31:55 -08:00
CONTRIBUTING.md Adding standard files (#302) 2018-02-26 10:31:55 -08:00
LICENSE.txt Updating license to Apache License 2.0 (#523) 2019-04-05 15:25:09 -07:00
NOTICE.txt Added Time Based Iterator Support 2016-08-11 09:20:25 -07:00
pom.xml Prepare for release v2.7.0 (#1445) 2025-03-11 11:17:36 -07:00
README.md Update documentation for upgrading to KCL v2.7.x (#1446) 2025-03-12 10:29:02 -07:00

Amazon Kinesis Client Library for Java

Build Status

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

Features

  • Provides an easy-to-use programming model for processing data using Amazon Kinesis
  • Helps with scale-out and fault-tolerant processing

Getting Started

  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'll 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 Developing Record Consumer Applications in the Amazon Kinesis Developer Guide.

Building from Source

After you've 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 -Dskip.ut=true.

Running Integration Tests

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.

Integration with the Kinesis Producer Library

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.

Amazon KCL support for other languages

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.

Using the KCL

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

🚨Important: Migration to KCL 2.7 or later with MultiLangDaemon - Credential Provider Changes Required

KCL version 2.7.0 and later uses AWS SDK for Java 2.x instead of AWS SDK for Java 1.x. All MultiLangDaemon users upgrading from earlier versions must update their credential provider configuration in the .properties file to use credentials provider name for AWS SDK for Java 2.x. Failure to do this will cause your multilang KCL application to fail during startup with credential provider construction errors. Please check the following link for the credentials provider mapping and MultiLangDaemon credentials provider configuration guide.

Version 2.x

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

Version 1.x

Version 1.x tracking branch

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

Release Notes

KCL Version Changelog
2.x master/CHANGELOG.md
1.x v1.x/CHANGELOG.md

Notices

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