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https://issues.apache.org/jira/browse/SPARK-14560?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15823296#comment-15823296
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Morten Hornbech commented on SPARK-14560:
-----------------------------------------
I have also observed this error sporadically on Spark 2.0.2. Does the
spark.shuffle.spill.numElementsForceSpillThreshold=N workaround work on 2.0.2?
Any experience with robust values?
Stack trace:
java.lang.OutOfMemoryError: Unable to acquire 36 bytes of memory, got 0
at
org.apache.spark.memory.MemoryConsumer.allocatePage(MemoryConsumer.java:129)
at
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPageIfNecessary(UnsafeExternalSorter.java:377)
at
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.insertRecord(UnsafeExternalSorter.java:399)
at
org.apache.spark.sql.execution.UnsafeExternalRowSorter.insertRow(UnsafeExternalRowSorter.java:94)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.sort_addToSorter$(Unknown
Source)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
Source)
at
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
at
org.apache.spark.sql.execution.RowIteratorFromScala.advanceNext(RowIterator.scala:83)
at
org.apache.spark.sql.execution.joins.SortMergeJoinScanner.advancedBufferedToRowWithNullFreeJoinKey(SortMergeJoinExec.scala:730)
at
org.apache.spark.sql.execution.joins.SortMergeJoinScanner.<init>(SortMergeJoinExec.scala:605)
at
org.apache.spark.sql.execution.joins.SortMergeJoinExec$$anonfun$doExecute$1.apply(SortMergeJoinExec.scala:162)
at
org.apache.spark.sql.execution.joins.SortMergeJoinExec$$anonfun$doExecute$1.apply(SortMergeJoinExec.scala:100)
at
org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:89)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at
org.apache.spark.rdd.CoalescedRDD$$anonfun$compute$1.apply(CoalescedRDD.scala:96)
at
org.apache.spark.rdd.CoalescedRDD$$anonfun$compute$1.apply(CoalescedRDD.scala:95)
at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at
org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$1$$anon$1.next(InMemoryRelation.scala:106)
at
org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$1$$anon$1.next(InMemoryRelation.scala:98)
at
org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:214)
at
org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:935)
at
org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:926)
at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:866)
at
org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:926)
at
org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:670)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:330)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:281)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
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)
> Cooperative Memory Management for Spillables
> --------------------------------------------
>
> Key: SPARK-14560
> URL: https://issues.apache.org/jira/browse/SPARK-14560
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.6.1
> Reporter: Imran Rashid
> Assignee: Lianhui Wang
> Fix For: 2.0.0
>
>
> SPARK-10432 introduced cooperative memory management for SQL operators that
> can spill; however, {{Spillable}} s used by the old RDD api still do not
> cooperate. This can lead to memory starvation, in particular on a
> shuffle-to-shuffle stage, eventually resulting in errors like:
> {noformat}
> 16/03/28 08:59:54 INFO memory.TaskMemoryManager: Memory used in task 3081
> 16/03/28 08:59:54 INFO memory.TaskMemoryManager: Acquired by
> org.apache.spark.shuffle.sort.ShuffleExternalSorter@69ab0291: 32.0 KB
> 16/03/28 08:59:54 INFO memory.TaskMemoryManager: 1317230346 bytes of memory
> were used by task 3081 but are not associated with specific consumers
> 16/03/28 08:59:54 INFO memory.TaskMemoryManager: 1317263114 bytes of memory
> are used for execution and 1710484 bytes of memory are used for storage
> 16/03/28 08:59:54 ERROR executor.Executor: Managed memory leak detected; size
> = 1317230346 bytes, TID = 3081
> 16/03/28 08:59:54 ERROR executor.Executor: Exception in task 533.0 in stage
> 3.0 (TID 3081)
> java.lang.OutOfMemoryError: Unable to acquire 75 bytes of memory, got 0
> at
> org.apache.spark.memory.MemoryConsumer.allocatePage(MemoryConsumer.java:120)
> at
> org.apache.spark.shuffle.sort.ShuffleExternalSorter.acquireNewPageIfNecessary(ShuffleExternalSorter.java:346)
> at
> org.apache.spark.shuffle.sort.ShuffleExternalSorter.insertRecord(ShuffleExternalSorter.java:367)
> at
> org.apache.spark.shuffle.sort.UnsafeShuffleWriter.insertRecordIntoSorter(UnsafeShuffleWriter.java:237)
> at
> org.apache.spark.shuffle.sort.UnsafeShuffleWriter.write(UnsafeShuffleWriter.java:164)
> 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:214)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:745)
> {noformat}
> This can happen anytime the shuffle read side requires more memory than what
> is available for the task. Since the shuffle-read side doubles its memory
> request each time, it can easily end up acquiring all of the available
> memory, even if it does not use it. Eg., say that after the final spill, the
> shuffle-read side requires 10 MB more memory, and there is 15 MB of memory
> available. But if it starts at 2 MB, it will double to 4, 8, and then
> request 16 MB of memory, and in fact get all available 15 MB. Since the 15
> MB of memory is sufficient, it will not spill, and will continue holding on
> to all available memory. But this leaves *no* memory available for the
> shuffle-write side. Since the shuffle-write side cannot request the
> shuffle-read side to free up memory, this leads to an OOM.
> The simple solution is to make {{Spillable}} implement {{MemoryConsumer}} as
> well, so RDDs can benefit from the cooperative memory management introduced
> by SPARK-10342.
> Note that an additional improvement would be for the shuffle-read side to
> simple release unused memory, without spilling, in case that would leave
> enough memory, and only spill if that was inadequate. However that can come
> as a later improvement.
> *Workaround*: You can set
> {{spark.shuffle.spill.numElementsForceSpillThreshold=N}} to force spilling to
> occur every {{N}} elements, thus preventing the shuffle-read side from ever
> grabbing all of the available memory. However, this requires careful tuning
> of {{N}} to specific workloads: too big, and you will still get an OOM; too
> small, and there will be so much spilling that performance will suffer
> drastically. Furthermore, this workaround uses an *undocumented*
> configuration with *no compatibility guarantees* for future versions of spark.
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