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https://issues.apache.org/jira/browse/BEAM-1074?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Aviem Zur updated BEAM-1074:
----------------------------
Description:
The SparkRunner uses {{mapWithState}} to read and manage CheckpointMarks, and
this stateful operation will be followed by a shuffle:
https://github.com/apache/spark/blob/master/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapWithStateDStream.scala#L159
Since the stateful read maps "splitSource" -> "partition of a list of read
values", the following shuffle won't benefit in any way (the list of read
values has not been flatMapped yet). In order to avoid shuffle we need to set
the input RDD ({{SourceRDD.Unbounded}}) partitioner to be a default
{{HashPartitioner}} since {{mapWithState}} would use the same partitioner and
will skip shuffle if the partitioners match.
was:This will make sure the following stateful read within {{mapWithState}}
won't shuffle the read values as long as they are grouped in a {{List}}.
> Set default-partitioner in SourceRDD.Unbounded.
> -----------------------------------------------
>
> Key: BEAM-1074
> URL: https://issues.apache.org/jira/browse/BEAM-1074
> Project: Beam
> Issue Type: Improvement
> Components: runner-spark
> Reporter: Amit Sela
> Assignee: Aviem Zur
> Fix For: First stable release
>
>
> The SparkRunner uses {{mapWithState}} to read and manage CheckpointMarks, and
> this stateful operation will be followed by a shuffle:
> https://github.com/apache/spark/blob/master/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapWithStateDStream.scala#L159
> Since the stateful read maps "splitSource" -> "partition of a list of read
> values", the following shuffle won't benefit in any way (the list of read
> values has not been flatMapped yet). In order to avoid shuffle we need to set
> the input RDD ({{SourceRDD.Unbounded}}) partitioner to be a default
> {{HashPartitioner}} since {{mapWithState}} would use the same partitioner and
> will skip shuffle if the partitioners match.
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