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ASF GitHub Bot logged work on BEAM-8470: ---------------------------------------- Author: ASF GitHub Bot Created on: 20/Nov/19 11:00 Start Date: 20/Nov/19 11:00 Worklog Time Spent: 10m Work Description: echauchot commented on issue #9866: [BEAM-8470] Create a new Spark runner based on Spark Structured streaming framework URL: https://github.com/apache/beam/pull/9866#issuecomment-555953424 @aromanenko-dev yes. The message in both LOG.info and javadoc is: > SparkStructuredStreamingRunner is based on spark structured streaming framework and is no more based on RDD/DStream API. See https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html It is still experimental, its coverage of the Beam model is partial. + @Experimental annotation ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org Issue Time Tracking ------------------- Worklog Id: (was: 346623) Time Spent: 10h 50m (was: 10h 40m) > Create a new Spark runner based on Spark Structured streaming framework > ----------------------------------------------------------------------- > > Key: BEAM-8470 > URL: https://issues.apache.org/jira/browse/BEAM-8470 > Project: Beam > Issue Type: Improvement > Components: runner-spark > Reporter: Etienne Chauchot > Assignee: Etienne Chauchot > Priority: Major > Time Spent: 10h 50m > Remaining Estimate: 0h > > h1. Why is it worth creating a new runner based on structured streaming: > Because this new framework brings: > * Unified batch and streaming semantics: > * no more RDD/DStream distinction, as in Beam (only PCollection) > * Better state management: > * incremental state instead of saving all each time > * No more synchronous saving delaying computation: per batch and partition > delta file saved asynchronously + in-memory hashmap synchronous put/get > * Schemas in datasets: > * The dataset knows the structure of the data (fields) and can optimize > later on > * Schemas in PCollection in Beam > * New Source API > * Very close to Beam bounded source and unbounded sources > h1. Why make a new runner from scratch? > * Structured streaming framework is very different from the RDD/Dstream > framework > h1. We hope to gain > * More up to date runner in terms of libraries: leverage new features > * Leverage learnt practices from the previous runners > * Better performance thanks to the DAG optimizer (catalyst) and by > simplifying the code. > * Simplify the code and ease the maintenance > -- This message was sent by Atlassian Jira (v8.3.4#803005)