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https://issues.apache.org/jira/browse/SPARK-11004?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14949235#comment-14949235
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Sean Owen commented on SPARK-11004:
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Per job, no I don't think so. It's a setting on the Spark configuration which
is fairly global. You might get away with shutting down the context and then
reinitializing a new one, but I think that might be incompatible with what
you're trying to test.
> MapReduce Hive-like join operations for RDDs
> --------------------------------------------
>
> Key: SPARK-11004
> URL: https://issues.apache.org/jira/browse/SPARK-11004
> Project: Spark
> Issue Type: New Feature
> Reporter: Glenn Strycker
>
> Could a feature be added to Spark that would use disk-only MapReduce
> operations for the very largest RDD joins?
> MapReduce is able to handle incredibly large table joins in a stable,
> predictable way with gracious failures and recovery. I have applications
> that are able to join 2 tables without error in Hive, but these same tables,
> when converted into RDDs, are unable to join in Spark (I am using the same
> cluster, and have played around with all of the memory configurations,
> persisting to disk, checkpointing, etc., and the RDDs are just too big for
> Spark on my cluster)
> So, Spark is usually able to handle fairly large RDD joins, but occasionally
> runs into problems when the tables are just too big (e.g. the notorious 2GB
> shuffle limit issue, memory problems, etc.) There are so many parameters to
> adjust (number of partitions, number of cores, memory per core, etc.) that it
> is difficult to guarantee stability on a shared cluster (say, running Yarn)
> with other jobs.
> Could a feature be added to Spark that would use disk-only MapReduce commands
> to do very large joins?
> That is, instead of myRDD1.join(myRDD2), we would have a special operation
> myRDD1.mapReduceJoin(myRDD2) that would checkpoint both RDDs to disk, run
> MapReduce, and then convert the results of the join back into a standard RDD.
> This might add stability for Spark jobs that deal with extremely large data,
> and enable developers to mix-and-match some Spark and MapReduce operations in
> the same program, rather than writing Hive scripts and stringing together
> Spark and MapReduce programs, which has extremely large overhead to convert
> RDDs to Hive tables and back again.
> Despite memory-level operations being where most of Spark's speed gains lie,
> sometimes using disk-only may help with stability!
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