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https://issues.apache.org/jira/browse/SPARK-11293?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15276723#comment-15276723
]
Miles Crawford commented on SPARK-11293:
----------------------------------------
Also biting us in 1.6.1 - we have to repartition our dataset into many
thousands of partitions to avoid the following stack:
{code}
2016-05-08 16:05:11,941 ERROR org.apache.spark.executor.Executor: Managed
memory leak detected; size = 5748783225 bytes, TID = 1283
2016-05-08 16:05:11,948 ERROR org.apache.spark.executor.Executor: Exception in
task 116.4 in stage 1.0 (TID 1283)
java.lang.OutOfMemoryError: Unable to acquire 2383 bytes of memory, got 0
at
org.apache.spark.memory.MemoryConsumer.allocatePage(MemoryConsumer.java:120)
~[d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
at
org.apache.spark.shuffle.sort.ShuffleExternalSorter.acquireNewPageIfNecessary(ShuffleExternalSorter.java:346)
~[d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
at
org.apache.spark.shuffle.sort.ShuffleExternalSorter.insertRecord(ShuffleExternalSorter.java:367)
~[d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
at
org.apache.spark.shuffle.sort.UnsafeShuffleWriter.insertRecordIntoSorter(UnsafeShuffleWriter.java:237)
~[d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
at
org.apache.spark.shuffle.sort.UnsafeShuffleWriter.write(UnsafeShuffleWriter.java:164)
~[d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
~[d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
~[d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
at org.apache.spark.scheduler.Task.run(Task.scala:89)
~[d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
~[d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
[na:1.8.0_91]
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
[na:1.8.0_91]
at java.lang.Thread.run(Thread.java:745) [na:1.8.0_91]{code}
> Spillable collections leak shuffle memory
> -----------------------------------------
>
> Key: SPARK-11293
> URL: https://issues.apache.org/jira/browse/SPARK-11293
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.3.1, 1.4.1, 1.5.1, 1.6.0, 1.6.1
> Reporter: Josh Rosen
> Assignee: Josh Rosen
> Priority: Critical
>
> I discovered multiple leaks of shuffle memory while working on my memory
> manager consolidation patch, which added the ability to do strict memory leak
> detection for the bookkeeping that used to be performed by the
> ShuffleMemoryManager. This uncovered a handful of places where tasks can
> acquire execution/shuffle memory but never release it, starving themselves of
> memory.
> Problems that I found:
> * {{ExternalSorter.stop()}} should release the sorter's shuffle/execution
> memory.
> * BlockStoreShuffleReader should call {{ExternalSorter.stop()}} using a
> {{CompletionIterator}}.
> * {{ExternalAppendOnlyMap}} exposes no equivalent of {{stop()}} for freeing
> its resources.
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