[jira] [Updated] (SPARK-9821) pyspark reduceByKey should allow a custom partitioner

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-9821?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen updated SPARK-9821:
-----------------------------
    Assignee: holdenk

> pyspark reduceByKey should allow a custom partitioner
> -----------------------------------------------------
>
>                 Key: SPARK-9821
>                 URL: https://issues.apache.org/jira/browse/SPARK-9821
>             Project: Spark
>          Issue Type: Improvement
>          Components: PySpark
>    Affects Versions: 1.3.0
>            Reporter: Diana Carroll
>            Assignee: holdenk
>            Priority: Minor
>             Fix For: 1.6.0
>
>
> In Scala, I can supply a custom partitioner to reduceByKey (and other aggregation/repartitioning methods like aggregateByKey and combinedByKey), but as far as I can tell from the Pyspark API, there's no way to do the same in Python.
> Here's an example of my code in Scala:
> {code}weblogs.map(s => (getFileType(s), 1)).reduceByKey(new FileTypePartitioner(),_+_){code}
> But I can't figure out how to do the same in Python.  The closest I can get is to call repartition before reduceByKey like so:
> {code}weblogs.map(lambda s: (getFileType(s), 1)).partitionBy(3,hash_filetype).reduceByKey(lambda v1,v2: v1+v2).collect(){code}
> But that defeats the purpose, because I'm shuffling twice instead of once, so my performance is worse instead of better.



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