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https://issues.apache.org/jira/browse/SPARK-19371?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16251971#comment-16251971
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Thunder Stumpges commented on SPARK-19371:
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Hi Sean,
>From my perspective, you can attach whatever label you want (bug, feature,
>story, PITA) but it is obviously causing troubles for more than just an
>isolated case.
And to re-state the issue, this is NOT (in my case at least) related to
partitioner / hashing. My partitions are sized evenly, the keys are unique
words (String) in a vocabulary. The issue is related to where the RDD blocks
are distributed to the executors in cache storage. See the image below which
shows the problem.
What would solve this for me is not really a "shuffle" but a "rebalance
storage" that moves RDD blocks in memory to balance them across executors.
!Unbalanced RDD Blocks, and resulting task imbalance.png!
> Cannot spread cached partitions evenly across executors
> -------------------------------------------------------
>
> Key: SPARK-19371
> URL: https://issues.apache.org/jira/browse/SPARK-19371
> Project: Spark
> Issue Type: Bug
> Affects Versions: 1.6.1
> Reporter: Thunder Stumpges
> Attachments: Unbalanced RDD Blocks, and resulting task imbalance.png,
> Unbalanced RDD Blocks, and resulting task imbalance.png
>
>
> Before running an intensive iterative job (in this case a distributed topic
> model training), we need to load a dataset and persist it across executors.
> After loading from HDFS and persisting, the partitions are spread unevenly
> across executors (based on the initial scheduling of the reads which are not
> data locale sensitive). The partition sizes are even, just not their
> distribution over executors. We currently have no way to force the partitions
> to spread evenly, and as the iterative algorithm begins, tasks are
> distributed to executors based on this initial load, forcing some very
> unbalanced work.
> This has been mentioned a
> [number|http://apache-spark-developers-list.1001551.n3.nabble.com/RDD-Partitions-not-distributed-evenly-to-executors-tt16988.html#a17059]
> of
> [times|http://apache-spark-user-list.1001560.n3.nabble.com/Spark-work-distribution-among-execs-tt26502.html]
> in
> [various|http://apache-spark-user-list.1001560.n3.nabble.com/Partitions-are-get-placed-on-the-single-node-tt26597.html]
> user/dev group threads.
> None of the discussions I could find had solutions that worked for me. Here
> are examples of things I have tried. All resulted in partitions in memory
> that were NOT evenly distributed to executors, causing future tasks to be
> imbalanced across executors as well.
> *Reduce Locality*
> {code}spark.shuffle.reduceLocality.enabled=false/true{code}
> *"Legacy" memory mode*
> {code}spark.memory.useLegacyMode = true/false{code}
> *Basic load and repartition*
> {code}
> val numPartitions = 48*16
> val df = sqlContext.read.
> parquet("/data/folder_to_load").
> repartition(numPartitions).
> persist
> df.count
> {code}
> *Load and repartition to 2x partitions, then shuffle repartition down to
> desired partitions*
> {code}
> val numPartitions = 48*16
> val df2 = sqlContext.read.
> parquet("/data/folder_to_load").
> repartition(numPartitions*2)
> val df = df2.repartition(numPartitions).
> persist
> df.count
> {code}
> It would be great if when persisting an RDD/DataFrame, if we could request
> that those partitions be stored evenly across executors in preparation for
> future tasks.
> I'm not sure if this is a more general issue (I.E. not just involving
> persisting RDDs), but for the persisted in-memory case, it can make a HUGE
> difference in the over-all running time of the remaining work.
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