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https://issues.apache.org/jira/browse/BEAM-4783?focusedWorklogId=182546&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-182546
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ASF GitHub Bot logged work on BEAM-4783:
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Author: ASF GitHub Bot
Created on: 08/Jan/19 16:58
Start Date: 08/Jan/19 16:58
Worklog Time Spent: 10m
Work Description: kyle-winkelman commented on issue #6884: [BEAM-4783]
Fix invalid parameter to set the partitioner in Spark GbK
URL: https://github.com/apache/beam/pull/6884#issuecomment-452372940
I believe this refactor actually does the opposite of what it was supposed
to. Previously the `HashPartitioner` was used in all cases. I wanted to get rid
of it but @iemejia was concerned it might bring back an old issue in which the
SparkRunner when in streaming mode would shuffle the data twice. I therefore
only removed the `HashPartitioner` in the case that bundleSize was specified.
Can someone check if a streaming workflow with a groupByKey has a double
shuffle? If not we can remove most of this code and always call
`rdd.groupByKey()` without the `HashPartitioner`. If it does we need to flip
all of this to do the opposite.
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Issue Time Tracking
-------------------
Worklog Id: (was: 182546)
Time Spent: 6h (was: 5h 50m)
> Add bundleSize parameter to control splitting of Spark sources (useful for
> Dynamic Allocation)
> ----------------------------------------------------------------------------------------------
>
> Key: BEAM-4783
> URL: https://issues.apache.org/jira/browse/BEAM-4783
> Project: Beam
> Issue Type: Improvement
> Components: runner-spark
> Affects Versions: 2.8.0
> Reporter: Kyle Winkelman
> Assignee: Kyle Winkelman
> Priority: Major
> Fix For: 2.8.0, 2.9.0
>
> Time Spent: 6h
> Remaining Estimate: 0h
>
> When the spark-runner is used along with the configuration
> spark.dynamicAllocation.enabled=true the SourceRDD does not detect this. It
> then falls back to the value calculated in this description:
> // when running on YARN/SparkDeploy it's the result of max(totalCores,
> 2).
> // when running on Mesos it's 8.
> // when running local it's the total number of cores (local = 1,
> local[N] = N,
> // local[*] = estimation of the machine's cores).
> // ** the configuration "spark.default.parallelism" takes precedence
> over all of the above **
> So in most cases this default is quite small. This is an issue when using a
> very large input file as it will only get split in half.
> I believe that when Dynamic Allocation is enable the SourceRDD should use the
> DEFAULT_BUNDLE_SIZE and possibly expose a SparkPipelineOptions that allows
> you to change this DEFAULT_BUNDLE_SIZE.
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