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https://issues.apache.org/jira/browse/SPARK-22568?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Éderson Cássio updated SPARK-22568:
-----------------------------------
Description:
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.
was:
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.
> 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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