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From "Andrew Palumbo (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MAHOUT-1790) SparkEngine nnz overflow resultSize when reducing.
Date Mon, 16 Jan 2017 03:23:26 GMT

    [ https://issues.apache.org/jira/browse/MAHOUT-1790?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15823400#comment-15823400
] 

Andrew Palumbo commented on MAHOUT-1790:
----------------------------------------

[~dlyubimov] Do you have any thoughts on this?  

> SparkEngine nnz overflow resultSize when reducing.
> --------------------------------------------------
>
>                 Key: MAHOUT-1790
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-1790
>             Project: Mahout
>          Issue Type: Bug
>          Components: spark
>    Affects Versions: 0.11.1
>            Reporter: Michel Lemay
>            Assignee: Andrew Palumbo
>            Priority: Minor
>             Fix For: 0.13.0
>
>
> When counting numNonZeroElementsPerColumn in spark engine with large number of columns,
we get the following error:
> ERROR TaskSetManager: Total size of serialized results of nnn tasks (1031.7 MB) is bigger
than spark.driver.maxResultSize (1024.0 MB)
> and then, the call stack:
> org.apache.spark.SparkException: Job aborted due to stage failure: Total size of serialized
results of 267 tasks (1024.1 MB) is bigger than spark.driver.maxResultSize (1024.0 MB)
>         at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1283)
>         at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1271)
>         at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1270)
>         at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>         at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
>         at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1270)
>         at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:697)
>         at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:697)
>         at scala.Option.foreach(Option.scala:236)
>         at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:697)
>         at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1496)
>         at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1458)
>         at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1447)
>         at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
>         at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:567)
>         at org.apache.spark.SparkContext.runJob(SparkContext.scala:1822)
>         at org.apache.spark.SparkContext.runJob(SparkContext.scala:1942)
>         at org.apache.spark.rdd.RDD$$anonfun$reduce$1.apply(RDD.scala:1003)
>         at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)
>         at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:108)
>         at org.apache.spark.rdd.RDD.withScope(RDD.scala:306)
>         at org.apache.spark.rdd.RDD.reduce(RDD.scala:985)
>         at org.apache.mahout.sparkbindings.SparkEngine$.numNonZeroElementsPerColumn(SparkEngine.scala:86)
>         at org.apache.mahout.math.drm.CheckpointedOps.numNonZeroElementsPerColumn(CheckpointedOps.scala:37)
>         at org.apache.mahout.math.cf.SimilarityAnalysis$.sampleDownAndBinarize(SimilarityAnalysis.scala:286)
>         at org.apache.mahout.math.cf.SimilarityAnalysis$.cooccurrences(SimilarityAnalysis.scala:66)
>         at org.apache.mahout.math.cf.SimilarityAnalysis$.cooccurrencesIDSs(SimilarityAnalysis.scala:141)
> This occurs because it uses a DenseVector and spark seemingly aggregate all of them on
the driver before reducing.  
> I think this could be easily prevented with a treeReduce(_ += _, depth)  instead of a
reduce(_ += _)
> 'depth' could be computed in function of 'n' and numberOfPartitions.. something in the
line of:
>   val maxResultSize = ....
>   val numPartitions = drm.rdd.partitions.size
>   val n = drm.ncol
>   val bytesPerVector = n * 8 + overhead?
>   val maxVectors = maxResultSize / bytes / 2 + 1 // be safe
>   val depth = math.max(1, math.ceil(math.log(1 + numPartitions / maxVectors) / math.log(2)).toInt)



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