* 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][docs-signup] in the AWS SDK for Java Developer Guide.
1.**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][kinesis-guide-create] in the Amazon Kinesis Developer Guide.
1.**Minimum requirements**— To use the Amazon Kinesis Client Library, you'll need **Java 1.7+**. For more information about Amazon Kinesis Client Library requirements, see [Before You Begin][kinesis-guide-begin] in the Amazon Kinesis Developer Guide.
1.**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][kinesis-guide-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`
For producer-side developers using the **[Kinesis Producer Library (KPL)][kinesis-guide-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][multi-lang-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][kclpy] 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.
* Expose [approximateArrivalTimestamp](http://docs.aws.amazon.com/kinesis/latest/APIReference/API_GetRecords.html) for Records in processRecords API call.
* Restores compatibility with [dynamodb-streams-kinesis-adapter](https://github.com/awslabs/dynamodb-streams-kinesis-adapter) (which was broken in 1.4.0).
* **Multi-Language Support** — Amazon KCL now supports implementing record processors in any language by communicating with the daemon over [STDIN and STDOUT][multi-lang-protocol]. Python developers can directly use the [Amazon Kinesis Client Library for Python][kclpy] to write their data processing applications.
* **Checkpointing at a specific sequence number** — The IRecordProcessorCheckpointer interface now supports checkpointing at a sequence number specified by the record processor.
* **Set region** — KinesisClientLibConfiguration now supports setting the region name to indicate the location of the Amazon Kinesis service. The Amazon DynamoDB table and Amazon CloudWatch metrics associated with your application will also use this region setting.
