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https://issues.apache.org/jira/browse/MAHOUT-1790?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Dmitriy Lyubimov updated MAHOUT-1790:
-------------------------------------
Sprint: Jan/Feb-2016 (was: Nov/Dec-2015)
> 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: Dmitriy Lyubimov
> Priority: Minor
> Fix For: 0.12.1
>
>
> 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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