[
https://issues.apache.org/jira/browse/SPARK-9872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Al M updated SPARK-9872:
------------------------
Description:
When I join two normal RDDs, I can set the number of shuffle partitions in the
'numPartitions' argument. When I join two DataFrames I do not have this option.
My spark job loads in 2 large files and 2 small files. When I perform a join,
this will use the "spark.sql.shuffle.partitions" to determine the number of
partitions. This means that the join with my small files will use exactly the
same number of partitions as the join with my large files.
I can either use a low number of partitions and run out of memory on my large
join, or use a high number of partitions and my small join will take far too
long.
If we were able to specify the number of shuffle partitions in a DataFrame join
like in an RDD join, this would not be an issue.
My long term ideal solution would be dynamic partition determination as
described in SPARK-4630. However I appreciate that it is not particularly easy
to do.
was:
When I join two normal RDDs, I can set the number of shuffle partitions in the
'numPartitions' argument. When I join two DataFrames I do not have this option.
My spark job loads in 2 large files and 2 small files. When I perform a join,
this will use the "spark.sql.shuffle.partitions" to determine the number of
partitions. This means that the join with my small files will use exactliy the
same number of partitions as the join with my large files.
I can either use a low number of partitions and run out of memory on my large
join, or use a high number of partitions and my small join will take far too
long.
If we were able to specify the number of shuffle partitions in a DataFrame join
like in an RDD join, this would not be an issue.
My long term ideal solution would be dynamic partition determination as
described in SPARK-4630. However I appreciate that it is not particularly easy
to do.
> Allow passing of 'numPartitions' to DataFrame joins
> ---------------------------------------------------
>
> Key: SPARK-9872
> URL: https://issues.apache.org/jira/browse/SPARK-9872
> Project: Spark
> Issue Type: Improvement
> Affects Versions: 1.4.1
> Reporter: Al M
> Priority: Minor
>
> When I join two normal RDDs, I can set the number of shuffle partitions in
> the 'numPartitions' argument. When I join two DataFrames I do not have this
> option.
> My spark job loads in 2 large files and 2 small files. When I perform a
> join, this will use the "spark.sql.shuffle.partitions" to determine the
> number of partitions. This means that the join with my small files will use
> exactly the same number of partitions as the join with my large files.
> I can either use a low number of partitions and run out of memory on my large
> join, or use a high number of partitions and my small join will take far too
> long.
> If we were able to specify the number of shuffle partitions in a DataFrame
> join like in an RDD join, this would not be an issue.
> My long term ideal solution would be dynamic partition determination as
> described in SPARK-4630. However I appreciate that it is not particularly
> easy to do.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
