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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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Sean Owen updated SPARK-20414:
------------------------------
Shepherd: (was: Sean Owen)
Flags: (was: Patch)
Affects Version/s: (was: 2.0.2)
(was: 2.0.1)
(was: 1.6.3)
(was: 1.6.2)
(was: 1.6.1)
(was: 1.5.2)
(was: 1.6.0)
(was: 2.0.0)
Target Version/s: (was: 2.1.0)
> 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: 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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