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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 -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org