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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2017/04/20 15:56:04 UTC
[jira] [Assigned] (SPARK-20414) avoid creating only 16 reducers
when calling topByKey()
[ https://issues.apache.org/jira/browse/SPARK-20414?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-20414:
------------------------------------
Assignee: (was: Apache Spark)
> 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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