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

["Diana Carroll (JIRA)" <ji...@apache.org>]

Diana Carroll created SPARK-9821:
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             Summary: pyspark reduceByKey should allow a custom partitioner
                 Key: SPARK-9821
                 URL: https://issues.apache.org/jira/browse/SPARK-9821
             Project: Spark
          Issue Type: Bug
          Components: PySpark
    Affects Versions: 1.3.0
            Reporter: Diana Carroll


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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