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https://issues.apache.org/jira/browse/SPARK-20414?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Yang Yang updated SPARK-20414:
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Flags: Patch
Description:
currently in the MLlib topByKey() function, it directly calls aggregateByKey(),
which by default uses very few partitions/reducers, in my experience I see only
16 reducers for a 100GB input.
the aggregateByKey() has an optional reducer count, adding this option to the
top level topByKey()
> avoid creating only 16 reducers when calling topByKey()
> -------------------------------------------------------
>
> Key: SPARK-20414
> URL: https://issues.apache.org/jira/browse/SPARK-20414
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.5.2, 1.6.0, 1.6.1, 1.6.2, 1.6.3, 2.0.0, 2.0.1, 2.0.2,
> 2.1.0
> Reporter: Yang Yang
> Priority: Minor
>
> currently in the MLlib topByKey() function, it directly calls
> aggregateByKey(), which by default uses very few partitions/reducers, in my
> experience I see only 16 reducers for a 100GB input.
> the aggregateByKey() has an optional reducer count, adding this option to the
> top level topByKey()
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