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SparkException caused by GC overhead limit exceeded

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author-rank carles_sanagust
New Contributor

Created ‎07-29-2016 08:45 AM

I am processing a Scala script on top of Spark and I get the next error message:

org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 4.0 failed 1 times, most recent failure: Lost task 0.0 in stage 4.0 (TID 40, localhost): java.lang.OutOfMemoryError: GC overhead limit exceeded
	at sun.reflect.GeneratedSerializationConstructorAccessor103.newInstance(Unknown Source)
	at java.lang.reflect.Constructor.newInstance(Constructor.java:422)
	at java.io.ObjectStreamClass.newInstance(ObjectStreamClass.java:967)
	at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1785)
	at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
	at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2000)
	at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1924)
	at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
	at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
	at java.io.ObjectInputStream.readArray(ObjectInputStream.java:1707)
	at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1345)
	at java.io.ObjectInputStream.readArray(ObjectInputStream.java:1707)
	at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1345)
	at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2000)
	at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1924)
	at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
	at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
	at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2000)
	at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1924)
	at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
	at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
	at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2000)
	at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1924)
	at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
	at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
	at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2000)
	at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1924)
	at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
	at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
	at java.io.ObjectInputStream.readObject(ObjectInputStream.java:371)
	at scala.collection.immutable.$colon$colon.readObject(List.scala:362)
	at sun.reflect.GeneratedMethodAccessor18.invoke(Unknown Source)
Driver stacktrace:
	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
	at scala.Option.foreach(Option.scala:236)
	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:927)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
	at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
	at org.apache.spark.rdd.RDD.collect(RDD.scala:926)
	at org.apache.spark.rdd.PairRDDFunctions$$anonfun$collectAsMap$1.apply(PairRDDFunctions.scala:741)
	at org.apache.spark.rdd.PairRDDFunctions$$anonfun$collectAsMap$1.apply(PairRDDFunctions.scala:740)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
	at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
	at org.apache.spark.rdd.PairRDDFunctions.collectAsMap(PairRDDFunctions.scala:740)
	at org.apache.spark.mllib.tree.DecisionTree$.findBestSplits(DecisionTree.scala:651)
	at org.apache.spark.mllib.tree.RandomForest.run(RandomForest.scala:233)
	at org.apache.spark.mllib.tree.RandomForest$.trainClassifier(RandomForest.scala:289)
	at org.apache.spark.mllib.tree.RandomForest$.trainClassifier(RandomForest.scala:331)
	at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:50)
	at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:55)
	at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:57)
	at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:59)
	at $iwC$$iwC$$iwC$$iwC.<init>(<console>:61)
	at $iwC$$iwC$$iwC.<init>(<console>:63)
	at $iwC$$iwC.<init>(<console>:65)
	at $iwC.<init>(<console>:67)
	at <init>(<console>:69)
	at .<init>(<console>:73)
	at .<clinit>(<console>)
	at .<init>(<console>:7)
	at .<clinit>(<console>)
	at $print(<console>)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:497)
	at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)
	at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1346)
	at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)
	at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)
	at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)
	at org.apache.zeppelin.spark.SparkInterpreter.interpretInput(SparkInterpreter.java:709)
	at org.apache.zeppelin.spark.SparkInterpreter.interpret(SparkInterpreter.java:673)
	at org.apache.zeppelin.spark.SparkInterpreter.interpret(SparkInterpreter.java:666)
	at org.apache.zeppelin.interpreter.ClassloaderInterpreter.interpret(ClassloaderInterpreter.java:57)
	at org.apache.zeppelin.interpreter.LazyOpenInterpreter.interpret(LazyOpenInterpreter.java:93)
	at org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer$InterpretJob.jobRun(RemoteInterpreterServer.java:295)
	at org.apache.zeppelin.scheduler.Job.run(Job.java:171)
	at org.apache.zeppelin.scheduler.FIFOScheduler$1.run(FIFOScheduler.java:139)
	at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
	at java.util.concurrent.FutureTask.run(FutureTask.java:266)
	at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180)
	at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
	at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.OutOfMemoryError: GC overhead limit exceeded
	at sun.reflect.GeneratedSerializationConstructorAccessor103.newInstance(Unknown Source)
	at java.lang.reflect.Constructor.newInstance(Constructor.java:422)
	at java.io.ObjectStreamClass.newInstance(ObjectStreamClass.java:967)
	at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1785)
	at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
	at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2000)
	at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1924)
	at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
	at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
	at java.io.ObjectInputStream.readArray(ObjectInputStream.java:1707)
	at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1345)
	at java.io.ObjectInputStream.readArray(ObjectInputStream.java:1707)
	at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1345)
	at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2000)
	at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1924)
	at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
	at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
	at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2000)
	at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1924)
	at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
	at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
	at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2000)
	at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1924)
	at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
	at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
	at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2000)
	at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1924)
	at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
	at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
	at java.io.ObjectInputStream.readObject(ObjectInputStream.java:371)
	at scala.collection.immutable.$colon$colon.readObject(List.scala:362)
	at sun.reflect.GeneratedMethodAccessor18.invoke(Unknown Source)

This is the executed code:

import org.apache.spark.mllib.tree.RandomForest
import org.apache.spark.mllib.tree.model.RandomForestModel
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.evaluation.MulticlassMetrics




val unparseddata = sc.textFile("hdfs:///tmp/new_data_binary_5.csv")
val data = unparseddata.map { line =>
  val parts = line.split(',').map(_.toDouble)
  LabeledPoint(parts.last%2, Vectors.dense(parts.slice(0, parts.length - 1)))
}


// Load and parse the data file.
//val data = MLUtils.loadLibSVMFile(sc, "/tmp/sample_libsvm_data.txt")
// Split the data into training and test sets (30% held out for testing)
val splits = data.randomSplit(Array(0.7, 0.3))
val (trainingData, testData) = (splits(0), splits(1))


// Train a RandomForest model.
// Empty categoricalFeaturesInfo indicates all features are continuous.
val numClasses = 3
val categoricalFeaturesInfo = Map[Int, Int]()
val numTrees = 3 // Use more in practice.
val featureSubsetStrategy = "auto" // Let the algorithm choose.
val impurity = "gini"
val maxDepth = 4
val maxBins = 32


val model = RandomForest.trainClassifier(trainingData, numClasses, categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins)


println("Learned classification forest model:\n" + model.toDebugString)


// Compute raw scores on the test set
val predictionAndLabels = test.map { case LabeledPoint(label, features) =>
  val prediction = model.predict(features)
  (prediction, label)
}


// Instantiate metrics object
val metrics = new MulticlassMetrics(predictionAndLabels)


// Confusion matrix
println("Confusion matrix:")
println(metrics.confusionMatrix)


// Overall Statistics
val precision = metrics.precision
val recall = metrics.recall // same as true positive rate
val f1Score = metrics.fMeasure
println("Summary Statistics")
println(s"Precision = $precision")
println(s"Recall = $recall")
println(s"F1 Score = $f1Score")


// Precision by label
val labels = metrics.labels
labels.foreach { l =>
  println(s"Precision($l) = " + metrics.precision(l))
}


// Recall by label
labels.foreach { l =>
  println(s"Recall($l) = " + metrics.recall(l))
}


// False positive rate by label
labels.foreach { l =>
  println(s"FPR($l) = " + metrics.falsePositiveRate(l))
}


// F-measure by label
labels.foreach { l =>
  println(s"F1-Score($l) = " + metrics.fMeasure(l))
}


// Weighted stats
println(s"Weighted precision: ${metrics.weightedPrecision}")
println(s"Weighted recall: ${metrics.weightedRecall}")
println(s"Weighted F1 score: ${metrics.weightedFMeasure}")
println(s"Weighted false positive rate: ${metrics.weightedFalsePositiveRate}")

We are running it from a Zeppelin Notebook.

I changed various parameters of Yarn and Spark configuration but still getting this message. Can some one help me solve this?

Thank you.

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4 REPLIES 4

avatar
author-rank aervits
Master Mentor

Created ‎07-29-2016 09:15 AM

Please provide your code and command to execute it

6,119 Views
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avatar
author-rank carles_sanagust
New Contributor

Created ‎07-29-2016 11:14 AM

message updated, thank you.

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avatar
author-rank jwiden
Super Collaborator

Created ‎08-01-2016 02:15 AM

If I had to guess your using Spark 1.5.2 or earlier. What is happening is you run out of memory. I think youre running out of executor memory, so you're probably doing a map-side aggregate. How many keys do you have? I think we can fix this pretty simply. Are you caching data? If not set spark.suffle.fraction to a number higher than .4.

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avatar
author-rank TimothySpann author-rank
Master Guru

Created ‎08-01-2016 02:37 AM

How much memory do you have? How much is assigned to Spark? Do you have logging on so you can check logs and history UI?

Turn off everything else you can.

For debugging run through the Spark shell, Zeppelin adds over head and takes a decent amount of YARN resources and RAM.

Run on Spark 1.6 / HDP 2.4.2 if you can. Allocate as much memory as possible. Spark is an all memory beast.

sparkConf.set("spark.cores.max", "16") // all the cores you can
sparkConf.set("spark.serializer", classOf[KryoSerializer].getName)
sparkConf.set("spark.sql.tungsten.enabled", "true")
sparkConf.set("spark.eventLog.enabled", "true")
sparkConf.set("spark.app.id", "YourID")
sparkConf.set("spark.io.compression.codec", "snappy")
sparkConf.set("spark.rdd.compress", "true")

I like to maximize my resources and performance.

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