Pravega Spark Connectors

Enable Spark to read and write Pravega streams

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This post introduces the Pravega Spark connectors that read and write Pravega Streams with Apache Spark, a high-performance analytics engine for batch and streaming data.

The connectors can be used to build end-to-end stream processing pipelines (see Samples) that use Pravega as the stream storage and message bus, and Apache Spark for computation over the streams.

Features & Highlights

  • Exactly-once processing guarantees for both Reader and Writer, supporting end-to-end exactly-once processing pipelines.

  • A Spark V2 data source micro-batch reader connector allows Spark Streaming applications to read Pravega Streams. Pravega stream cuts are used to reliably recover from failures and provide exactly-once semantics.

  • A Spark base relation data source batch reader connector allows Spark batch applications to read Pravega Streams.

  • A Spark V2 data source stream writer allows Spark Streaming applications to write to Pravega Streams. Writes are contained within Pravega transactions, providing exactly-once semantics.

  • Seamless integration with Spark‚Äôs checkpoints.

  • Parallel Readers and Writers supporting high throughput and low latency processing.

  • Reader supports reassembling chunked events to support events of 2 GiB.

Limitations

  • The current implementation of this connector does not guarantee that events with the same routing key are returned in a single partition. If your application requires this, you must repartition the dataframe by the routing key and sort within the partition by segment_id and offset.

  • Continuous reader support is not available. The micro-batch reader uses the Pravega batch API and works well for applications with latency requirements above 100 milliseconds.

  • The initial batch in the micro-batch reader will contain the entire Pravega stream as of the start time. There is no rate limiting functionality.

  • Read-after-write consistency is currently not guaranteed. Be cautious if your workflow requires multiple chained Spark batch jobs.

Build and Install the Spark Connector

This will build the Spark Connector and publish it to your local Maven repository.

cd
git clone https://github.com/pravega/spark-connectors
cd spark-connectors
./gradlew install
ls -lhR ~/.m2/repository/io/pravega/pravega-connectors-spark

Source

https://github.com/pravega/spark-connectors

Documentation

To learn more about how to build and use the Spark Connector library, refer to Pravega Samples.

Reference

http://blog.madhukaraphatak.com/spark-datasource-v2-part-1/

License

Spark connectors for Pravega is 100% open source and community-driven. All components are available under Apache 2 License on GitHub.

Post on 18 Mar 2020