Details
-
Bug
-
Status: Closed
-
Major
-
Resolution: Information Provided
-
1.6.2
-
None
-
None
Description
For a while now, I have noticed messages like
16/10/31 09:44:25 ERROR Executor: Managed memory leak detected; size = 5251642 bytes, TID = 64204
when running jobs with spark 1.6.2.
I have seen others post bugs on this, but they are all marked fixed in earlier versions. I am certain that the issue still exists in 1.6.2.
In the following case, I can even get it to leak 2.7G all at once.
The message is:
16/10/31 11:12:47 ERROR Executor: Managed memory leak detected; size = 2724723111 bytes, TID = 18
The code snippet causing it is:
val nonZeros: RDD[((Int, Float), Array[Long])] = featureValues.map(y => (y._1._1 + "," + y._1._2, y._2)).reduceByKey { case (v1, v2) => (v1, v2).zipped.map(_ + _) }.map(y => { val s = y._1.split(",") ((s(0).toInt, s(1).toFloat), y._2) })
and the stack trace is:
16/10/31 11:12:47 ERROR Executor: Exception in task 0.0 in stage 11.0 (TID 18) java.lang.OutOfMemoryError: Java heap space at scala.collection.mutable.ArrayBuilder$ofLong.mkArray(ArrayBuilder.scala:388) at scala.collection.mutable.ArrayBuilder$ofLong.resize(ArrayBuilder.scala:394) at scala.collection.mutable.ArrayBuilder$ofLong.sizeHint(ArrayBuilder.scala:399) at scala.collection.mutable.Builder$class.sizeHint(Builder.scala:69) at scala.collection.mutable.ArrayBuilder.sizeHint(ArrayBuilder.scala:22) at scala.runtime.Tuple2Zipped$.map$extension(Tuple2Zipped.scala:41) at org.apache.spark.mllib.feature.MDLPDiscretizer$$anonfun$11.apply(MDLPDiscretizer.scala:151) at org.apache.spark.mllib.feature.MDLPDiscretizer$$anonfun$11.apply(MDLPDiscretizer.scala:150) at org.apache.spark.util.collection.ExternalSorter$$anonfun$5.apply(ExternalSorter.scala:187) at org.apache.spark.util.collection.ExternalSorter$$anonfun$5.apply(ExternalSorter.scala:186) at org.apache.spark.util.collection.AppendOnlyMap.changeValue(AppendOnlyMap.scala:144) at org.apache.spark.util.collection.SizeTrackingAppendOnlyMap.changeValue(SizeTrackingAppendOnlyMap.scala:32) at org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:192) at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:64) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) at org.apache.spark.scheduler.Task.run(Task.scala:89) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) 16/10/31 11:12:47 ERROR SparkUncaughtExceptionHandler: Uncaught exception in thread Thread[Executor task launch worker-0,5,main] java.lang.OutOfMemoryError: Java heap space at scala.collection.mutable.ArrayBuilder$ofLong.mkArray(ArrayBuilder.scala:388) at scala.collection.mutable.ArrayBuilder$ofLong.resize(ArrayBuilder.scala:394) at scala.collection.mutable.ArrayBuilder$ofLong.sizeHint(ArrayBuilder.scala:399) at scala.collection.mutable.Builder$class.sizeHint(Builder.scala:69) at scala.collection.mutable.ArrayBuilder.sizeHint(ArrayBuilder.scala:22) at scala.runtime.Tuple2Zipped$.map$extension(Tuple2Zipped.scala:41) at org.apache.spark.mllib.feature.MDLPDiscretizer$$anonfun$11.apply(MDLPDiscretizer.scala:151) at org.apache.spark.mllib.feature.MDLPDiscretizer$$anonfun$11.apply(MDLPDiscretizer.scala:150) at org.apache.spark.util.collection.ExternalSorter$$anonfun$5.apply(ExternalSorter.scala:187) at org.apache.spark.util.collection.ExternalSorter$$anonfun$5.apply(ExternalSorter.scala:186) at org.apache.spark.util.collection.AppendOnlyMap.changeValue(AppendOnlyMap.scala:144) at org.apache.spark.util.collection.SizeTrackingAppendOnlyMap.changeValue(SizeTrackingAppendOnlyMap.scala:32) at org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:192) at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:64) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) at org.apache.spark.scheduler.Task.run(Task.scala:89) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) 16/10/31 11:12:47 WARN TaskSetManager: Lost task 0.0 in stage 11.0 (TID 18, localhost): java.lang.OutOfMemoryError: Java heap space at scala.collection.mutable.ArrayBuilder$ofLong.mkArray(ArrayBuilder.scala:388) at scala.collection.mutable.ArrayBuilder$ofLong.resize(ArrayBuilder.scala:394) at scala.collection.mutable.ArrayBuilder$ofLong.sizeHint(ArrayBuilder.scala:399) at scala.collection.mutable.Builder$class.sizeHint(Builder.scala:69) at scala.collection.mutable.ArrayBuilder.sizeHint(ArrayBuilder.scala:22) at scala.runtime.Tuple2Zipped$.map$extension(Tuple2Zipped.scala:41) at org.apache.spark.mllib.feature.MDLPDiscretizer$$anonfun$11.apply(MDLPDiscretizer.scala:151) at org.apache.spark.mllib.feature.MDLPDiscretizer$$anonfun$11.apply(MDLPDiscretizer.scala:150) at org.apache.spark.util.collection.ExternalSorter$$anonfun$5.apply(ExternalSorter.scala:187) at org.apache.spark.util.collection.ExternalSorter$$anonfun$5.apply(ExternalSorter.scala:186) at org.apache.spark.util.collection.AppendOnlyMap.changeValue(AppendOnlyMap.scala:144) at org.apache.spark.util.collection.SizeTrackingAppendOnlyMap.changeValue(SizeTrackingAppendOnlyMap.scala:32) at org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:192) at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:64) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) at org.apache.spark.scheduler.Task.run(Task.scala:89)
If you want to run the example, you can checkout this branch of the MDLP project
https://github.com/barrybecker4/spark-MDLP-discretization/tree/ISSUE-14-performance
and going to commit cd9c797 and running the MDLPDiscretizerHugeSuite.
Attachments
Issue Links
- is related to
-
SPARK-14363 Executor OOM due to a memory leak in Sorter
- Resolved