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https://issues.apache.org/jira/browse/SPARK-14363?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15622975#comment-15622975
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Barry Becker commented on SPARK-14363:
--------------------------------------
I am hitting this issue in 1.6.2.
In fact, I can make a case where it will leek 2.7G in one shot when doing
reduceByKey. I will open a separate bug.
> Executor OOM due to a memory leak in Sorter
> -------------------------------------------
>
> Key: SPARK-14363
> URL: https://issues.apache.org/jira/browse/SPARK-14363
> Project: Spark
> Issue Type: Bug
> Components: Shuffle
> Affects Versions: 1.6.1
> Reporter: Sital Kedia
> Assignee: Sital Kedia
> Fix For: 1.6.2, 2.0.0
>
>
> While running a Spark job, we see that the job fails because of executor OOM
> with following stack trace -
> {code}
> java.lang.OutOfMemoryError: Unable to acquire 76 bytes of memory, got 0
> at
> org.apache.spark.memory.MemoryConsumer.allocatePage(MemoryConsumer.java:120)
> at
> org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPageIfNecessary(UnsafeExternalSorter.java:326)
> at
> org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.insertRecord(UnsafeExternalSorter.java:341)
> at
> org.apache.spark.sql.execution.UnsafeExternalRowSorter.insertRow(UnsafeExternalRowSorter.java:91)
> at
> org.apache.spark.sql.execution.UnsafeExternalRowSorter.sort(UnsafeExternalRowSorter.java:168)
> at org.apache.spark.sql.execution.Sort$$anonfun$1.apply(Sort.scala:90)
> at org.apache.spark.sql.execution.Sort$$anonfun$1.apply(Sort.scala:64)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
> at org.apache.spark.scheduler.Task.run(Task.scala:89)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> 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)
> {code}
> The issue is that there is a memory leak in the Sorter. When the
> UnsafeExternalSorter spills the data to disk, it does not free up the
> underlying pointer array. As a result, we see a lot of executor OOM and also
> memory under utilization.
> This is a regression partially introduced in PR
> https://github.com/apache/spark/pull/9241
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