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From "Matthias Boehm (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SYSTEMML-2013) Perftest genStratStatsData failed for 80GB
Date Sun, 12 Nov 2017 06:10:00 GMT

     [ https://issues.apache.org/jira/browse/SYSTEMML-2013?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Matthias Boehm resolved SYSTEMML-2013.
--------------------------------------
       Resolution: Fixed
         Assignee: Matthias Boehm
    Fix Version/s: SystemML 1.0

> Perftest genStratStatsData failed for 80GB
> ------------------------------------------
>
>                 Key: SYSTEMML-2013
>                 URL: https://issues.apache.org/jira/browse/SYSTEMML-2013
>             Project: SystemML
>          Issue Type: Bug
>            Reporter: Matthias Boehm
>            Assignee: Matthias Boehm
>             Fix For: SystemML 1.0
>
>
> {code}
> Caused by: org.apache.sysml.runtime.DMLRuntimeException: ERROR: Runtime error in program
block generated from statement block between lines 107 and 0 -- Error evaluating instruction:
SPARK°write°_mVar123·MATRIX·DOUBLE°mbperftest/stratstats/A_10M/data·SCALAR·STRING·true°binaryblock·SCALAR·STRING·true°·SCALAR·STRING·true
> 	at org.apache.sysml.runtime.controlprogram.ProgramBlock.executeSingleInstruction(ProgramBlock.java:294)
> 	at org.apache.sysml.runtime.controlprogram.ProgramBlock.executeInstructions(ProgramBlock.java:218)
> 	at org.apache.sysml.runtime.controlprogram.ProgramBlock.execute(ProgramBlock.java:163)
> 	at org.apache.sysml.runtime.controlprogram.Program.execute(Program.java:118)
> 	... 13 more
> Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Serialized
task 15:2 was 323397641 bytes, which exceeds max allowed: spark.rpc.message.maxSize (134217728
bytes). Consider increasing spark.rpc.message.maxSize or using broadcast variables for large
values.
> 	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)
> 	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> 	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> 	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
> 	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
> 	at scala.Option.foreach(Option.scala:257)
> 	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)
> 	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)
> 	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)
> 	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)
> 	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> 	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)
> 	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1918)
> 	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1931)
> 	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1951)
> 	at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1.apply$mcV$sp(PairRDDFunctions.scala:1226)
> 	at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1.apply(PairRDDFunctions.scala:1168)
> 	at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1.apply(PairRDDFunctions.scala:1168)
> 	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> 	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> 	at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
> 	at org.apache.spark.rdd.PairRDDFunctions.saveAsHadoopDataset(PairRDDFunctions.scala:1168)
> 	at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopFile$4.apply$mcV$sp(PairRDDFunctions.scala:1071)
> 	at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopFile$4.apply(PairRDDFunctions.scala:1037)
> 	at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopFile$4.apply(PairRDDFunctions.scala:1037)
> 	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> 	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> 	at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
> 	at org.apache.spark.rdd.PairRDDFunctions.saveAsHadoopFile(PairRDDFunctions.scala:1037)
> 	at org.apache.spark.api.java.JavaPairRDD.saveAsHadoopFile(JavaPairRDD.scala:803)
> 	at org.apache.sysml.runtime.instructions.spark.WriteSPInstruction.processMatrixWriteInstruction(WriteSPInstruction.java:218)
> 	at org.apache.sysml.runtime.instructions.spark.WriteSPInstruction.processInstruction(WriteSPInstruction.java:144)
> 	at org.apache.sysml.runtime.controlprogram.ProgramBlock.executeSingleInstruction(ProgramBlock.java:264)
> {code}



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