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https://issues.apache.org/jira/browse/BEAM-4783?focusedWorklogId=144336&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-144336
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ASF GitHub Bot logged work on BEAM-4783:
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Author: ASF GitHub Bot
Created on: 14/Sep/18 16:34
Start Date: 14/Sep/18 16:34
Worklog Time Spent: 10m
Work Description: lukecwik commented on issue #6181: [BEAM-4783] Add
bundleSize for splitting BoundedSources.
URL: https://github.com/apache/beam/pull/6181#issuecomment-421414299
I don't believe the Dataflow worker code is very useful for dynamic work
rebalancing. Good dynamic work rebalancing will need support/signals from each
runner. I believe there is a way to build simple dynamic work rebalancing
system that would work for all bounded splits by performing a limited amount of
graph rewriting at pipeline submission time and then periodic splitting while
running sources. You need support for a self loop within the runner to be able
to get support for unbounded soruces.
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Issue Time Tracking
-------------------
Worklog Id: (was: 144336)
Time Spent: 2.5h (was: 2h 20m)
> Spark SourceRDD Not Designed With Dynamic Allocation In Mind
> ------------------------------------------------------------
>
> Key: BEAM-4783
> URL: https://issues.apache.org/jira/browse/BEAM-4783
> Project: Beam
> Issue Type: Improvement
> Components: runner-spark
> Affects Versions: 2.5.0
> Reporter: Kyle Winkelman
> Assignee: Jean-Baptiste Onofré
> Priority: Major
> Labels: newbie
> Time Spent: 2.5h
> 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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