[jira] [Created] (SPARK-22568) Split pair RDDs by keys - an efficient (maybe?) substitute to groupByKey

["Éderson Cássio (JIRA)" <ji...@apache.org>]

Éderson Cássio created SPARK-22568:
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             Summary: Split pair RDDs by keys - an efficient (maybe?) substitute to groupByKey
                 Key: SPARK-22568
                 URL: https://issues.apache.org/jira/browse/SPARK-22568
             Project: Spark
          Issue Type: New Feature
          Components: Spark Core
    Affects Versions: 2.2.0
            Reporter: Éderson Cássio


Sorry for any mistakes on filling this big form... it's my first issue here :)
Recently, I have the need to separate a RDD by some categorization. I was able to accomplish that by some ways.
First, the obvious: mapping each element to a pair, with the key being the category of the element. Then, using the good ol' {{groupByKey}}.
Listening to advices to avoid {{groupByKey}}, I failed to find another way that was more efficient. I ended up (a) obtaining the distinct list of element categories, (b) {{collect}}ing them and (c) making a call to {{filter}} for each category. Of course, before all I {{cache}}d my initial RDD.
So, I started to speculate: maybe it would be possible to make a number of RDDs from an initial pair RDD _without the need to shuffle the data_. It could be made by a kind of _local repartition_: first each partition is splitted into various by key; then the master group the partitions with the same key into a new RDD. The operation returns a List or array containing the new RDDs.
It's just a conjecture, I don't know if it would be feasible in current Spark Core architecture. But it would be great if it could be done.



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