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

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

    [ https://issues.apache.org/jira/browse/SPARK-22568?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16260616#comment-16260616 ] 

Éderson Cássio commented on SPARK-22568:
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Thanks for your response.
How can I filter by each distinct key without know them previously? It's common to determine the keys by some processing routine. The only way I could be able to filter by all of them is doing a {{collect}}, before this I don't ever know how many they are.

> 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
>              Labels: features, performance, usability
>
> 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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