The Kinesis receiver creates an input DStream using the Kinesis Client Library (KCL) provided by Amazon under the Amazon Software License (ASL).
The KCL builds on top of the Apache 2.0 licensed AWS Java SDK and provides load-balancing, fault-tolerance, checkpointing through the concepts of Workers, Checkpoints, and Shard Leases.
1.**Linking:** In your SBT/Maven project definition, link your streaming application against the following artifact (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide for further information).
and the [example]({{site.SPARK_GITHUB_URL}}/tree/master/extras/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala). Refer to the Running the Example section for instructions on how to run the example.
See the [API docs](api/java/index.html?org/apache/spark/streaming/kinesis/KinesisUtils.html)
and the [example]({{site.SPARK_GITHUB_URL}}/tree/master/extras/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java). Refer to the next subsection for instructions to run the example.
-`[region name]`: Valid Kinesis region names can be found [here](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-regions-availability-zones.html).
-`[checkpoint interval]`: The interval (e.g., Duration(2000) = 2 seconds) at which the Kinesis Client Library saves its position in the stream. For starters, set it to the same as the batch interval of the streaming application.
-`[initial position]`: Can be either `InitialPositionInStream.TRIM_HORIZON` or `InitialPositionInStream.LATEST` (see Kinesis Checkpointing section and Amazon Kinesis API documentation for more details).
3.**Deploying:** Package `spark-streaming-kinesis-asl_{{site.SCALA_BINARY_VERSION}}` and its dependencies (except `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` which are provided by `spark-submit`) into the application JAR. Then use `spark-submit` to launch your application (see [Deploying section](streaming-programming-guide.html#deploying-applications) in the main programming guide).
*Points to remember at runtime:*
- Kinesis data processing is ordered per partition and occurs at-least once per message.
- Multiple applications can read from the same Kinesis stream. Kinesis will maintain the application-specific shard and checkpoint info in DynamodDB.
- A single Kinesis stream shard is processed by one input DStream at a time.
<pstyle="text-align: center;">
<imgsrc="img/streaming-kinesis-arch.png"
title="Spark Streaming Kinesis Architecture"
alt="Spark Streaming Kinesis Architecture"
width="60%"
/>
<!-- Images are downsized intentionally to improve quality on retina displays -->
</p>
- A single Kinesis input DStream can read from multiple shards of a Kinesis stream by creating multiple KinesisRecordProcessor threads.
- Multiple input DStreams running in separate processes/instances can read from a Kinesis stream.
- You never need more Kinesis input DStreams than the number of Kinesis stream shards as each input DStream will create at least one KinesisRecordProcessor thread that handles a single shard.
- Horizontal scaling is achieved by adding/removing Kinesis input DStreams (within a single process or across multiple processes/instances) - up to the total number of Kinesis stream shards per the previous point.
- The Kinesis input DStream will balance the load between all DStreams - even across processes/instances.
- The Kinesis input DStream will balance the load during re-shard events (merging and splitting) due to changes in load.
- As a best practice, it's recommended that you avoid re-shard jitter by over-provisioning when possible.
- Each Kinesis input DStream maintains its own checkpoint info. See the Kinesis Checkpointing section for more details.
- There is no correlation between the number of Kinesis stream shards and the number of RDD partitions/shards created across the Spark cluster during input DStream processing. These are 2 independent partitioning schemes.
- Set up Kinesis stream (see earlier section) within AWS. Note the name of the Kinesis stream and the endpoint URL corresponding to the region where the stream was created.
This will push 1000 lines per second of 10 random numbers per line to the Kinesis stream. This data should then be received and processed by the running example.
- Each Kinesis input DStream periodically stores the current position of the stream in the backing DynamoDB table. This allows the system to recover from failures and continue processing where the DStream left off.
- Checkpointing too frequently will cause excess load on the AWS checkpoint storage layer and may lead to AWS throttling. The provided example handles this throttling with a random-backoff-retry strategy.
- If no Kinesis checkpoint info exists when the input DStream starts, it will start either from the oldest record available (InitialPositionInStream.TRIM_HORIZON) or from the latest tip (InitialPostitionInStream.LATEST). This is configurable.
- InitialPositionInStream.LATEST could lead to missed records if data is added to the stream while no input DStreams are running (and no checkpoint info is being stored).
- InitialPositionInStream.TRIM_HORIZON may lead to duplicate processing of records where the impact is dependent on checkpoint frequency and processing idempotency.