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https://issues.apache.org/jira/browse/BEAM-7413?focusedWorklogId=253060&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-253060
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ASF GitHub Bot logged work on BEAM-7413:
----------------------------------------
Author: ASF GitHub Bot
Created on: 03/Jun/19 12:04
Start Date: 03/Jun/19 12:04
Worklog Time Spent: 10m
Work Description: pbackx commented on pull request #8743: [BEAM-7413]
Fixes bug in partitioning of GroupNonMergingWindowsFunctions
URL: https://github.com/apache/beam/pull/8743
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Issue Time Tracking
-------------------
Worklog Id: (was: 253060)
Time Spent: 10m
Remaining Estimate: 0h
> Huge amount of tasks per stage in Spark runner after upgrade to Beam 2.12.0
> ---------------------------------------------------------------------------
>
> Key: BEAM-7413
> URL: https://issues.apache.org/jira/browse/BEAM-7413
> Project: Beam
> Issue Type: Bug
> Components: runner-spark
> Affects Versions: 2.12.0
> Reporter: Peter Backx
> Priority: Major
> Time Spent: 10m
> Remaining Estimate: 0h
>
> After upgrading from Beam 2.8.0 to 2.12.0 we see a huge number of tasks per
> stage in our pipelines. Where we used to see a few thousands tasks/stage at
> most, it's now into the millions. This makes the pipeline unable to complete
> successfully (driver and network are overloaded)
> It looks like after each (Co)GroupByKey operation, the amount of tasks (per
> stage) at least doubles sometimes even more.
> I did notice a fix to GroupByKey (BEAM-5392) that may or may not be related.
> I cannot post the full pipeline, but we have created a small test to showcase
> the effect:
> [https://github.com/pbackx/beam-groupbykey-test]
>
> [https://github.com/pbackx/beam-groupbykey-test/blob/master/src/test/java/NumTaskTest.java]
> contains two tests:
> * One shows how we would usually join PCollections together and if you run
> it, you'll see the number of tasks gradually increase
> * The other uses a GroupIntoBatches operation after each join. The effect is
> that there's no longer an increase in tasks. (the deprecated Reshuffle
> operation has a similar effect, but it's deprecated...)
> We've now sprinkled GroupIntoBatches throughout our pipeline and this seems
> to avoid the issue, but at the cost of performance (this effect is much worse
> in the toy example than in our "real" pipeline to be honest).
> My questions:
> * Is this a bug or is this expected behavior?
> * Is the GroupIntoBatches the best workaround or are there better options?
>
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