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https://issues.apache.org/jira/browse/SPARK-17204?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15791884#comment-15791884
]
Michael Allman commented on SPARK-17204:
----------------------------------------
I'm 99% sure I've fixed this. I'll submit a PR in the coming days.
> Spark 2.0 off heap RDD persistence with replication factor 2 leads to
> in-memory data corruption
> -----------------------------------------------------------------------------------------------
>
> Key: SPARK-17204
> URL: https://issues.apache.org/jira/browse/SPARK-17204
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.0.0
> Reporter: Michael Allman
>
> We use the {{OFF_HEAP}} storage level extensively with great success. We've
> tried off-heap storage with replication factor 2 and have always received
> exceptions on the executor side very shortly after starting the job. For
> example:
> {code}
> com.esotericsoftware.kryo.KryoException: Encountered unregistered class ID:
> 9086
> at
> com.esotericsoftware.kryo.util.DefaultClassResolver.readClass(DefaultClassResolver.java:137)
> at com.esotericsoftware.kryo.Kryo.readClass(Kryo.java:670)
> at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:781)
> at
> org.apache.spark.serializer.KryoDeserializationStream.readObject(KryoSerializer.scala:229)
> at
> org.apache.spark.serializer.DeserializationStream$$anon$1.getNext(Serializer.scala:169)
> at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
> at
> org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
> at
> org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:461)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificColumnarIterator.hasNext(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.agg_doAggregateWithoutKey$(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> at
> org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
> at org.apache.spark.scheduler.Task.run(Task.scala:85)
> at org.apache.spark.executor.Executor$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)
> {code}
> or
> {code}
> java.lang.IndexOutOfBoundsException: Index: 6, Size: 0
> at java.util.ArrayList.rangeCheck(ArrayList.java:653)
> at java.util.ArrayList.get(ArrayList.java:429)
> at
> com.esotericsoftware.kryo.util.MapReferenceResolver.getReadObject(MapReferenceResolver.java:60)
> at com.esotericsoftware.kryo.Kryo.readReferenceOrNull(Kryo.java:834)
> at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:788)
> at
> org.apache.spark.serializer.KryoDeserializationStream.readObject(KryoSerializer.scala:229)
> at
> org.apache.spark.serializer.DeserializationStream$$anon$1.getNext(Serializer.scala:169)
> at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
> at
> org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
> at
> org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:461)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificColumnarIterator.hasNext(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.agg_doAggregateWithoutKey$(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> at
> org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
> at org.apache.spark.scheduler.Task.run(Task.scala:85)
> at org.apache.spark.executor.Executor$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)
> {code}
> or
> {code}
> java.lang.NullPointerException
> at
> org.apache.spark.sql.execution.columnar.InMemoryTableScanExec$$anonfun$doExecute$1$$anonfun$6.apply(InMemoryTableScanExec.scala:141)
> at
> org.apache.spark.sql.execution.columnar.InMemoryTableScanExec$$anonfun$doExecute$1$$anonfun$6.apply(InMemoryTableScanExec.scala:140)
> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:463)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificColumnarIterator.hasNext(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificColumnarIterator.hasNext(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.agg_doAggregateWithoutKey$(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$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 scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> at
> org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
> at org.apache.spark.scheduler.Task.run(Task.scala:85)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> {code}
> We've tried switching to Java serialization and get a different exception:
> {code}
> java.io.StreamCorruptedException: invalid stream header: 780000D0
> at
> java.io.ObjectInputStream.readStreamHeader(ObjectInputStream.java:808)
> at java.io.ObjectInputStream.<init>(ObjectInputStream.java:301)
> at
> org.apache.spark.serializer.JavaDeserializationStream$$anon$1.<init>(JavaSerializer.scala:63)
> at
> org.apache.spark.serializer.JavaDeserializationStream.<init>(JavaSerializer.scala:63)
> at
> org.apache.spark.serializer.JavaSerializerInstance.deserializeStream(JavaSerializer.scala:122)
> at
> org.apache.spark.serializer.SerializerManager.dataDeserializeStream(SerializerManager.scala:146)
> at
> org.apache.spark.storage.BlockManager.getLocalValues(BlockManager.scala:433)
> at
> org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:672)
> at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:330)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:281)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
> at org.apache.spark.scheduler.Task.run(Task.scala:85)
> at org.apache.spark.executor.Executor$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)
> {code}
> This suggest some kind of memory corruption to me.
> I've been able to consistently reproduce this problem in local-cluster mode
> with a very simple code snippet. First, start a spark shell like this:
> {noformat}
> MASTER=local-cluster[2,1,1024] ./spark-2.1/bin/spark-shell --conf
> spark.memory.offHeap.enabled=true --conf spark.memory.offHeap.size=1024
> {noformat}
> Then run the following:
> {code}
> import org.apache.spark.storage.StorageLevel
> val OFF_HEAP_2 = StorageLevel(useDisk = true, useMemory = true, useOffHeap =
> true, deserialized = false, replication = 2)
> sc.range(0, 100).persist(OFF_HEAP_2).count
> {code}
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