Michael Allman created SPARK-17204:
--------------------------------------
Summary: Spark 2.0 off heap RDD persistence with replication
factor 2 leads to 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. 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$$anon$11.hasNext(Iterator.scala:408)
at
org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
at org.apache.spark.scheduler.Task.run(Task.scala:85)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
We've tried switching to Java serialization and get a different exception:
java.io.StreamCorruptedException: invalid stream header: 780000D0
at
java.io.ObjectInputStream.readStreamHeader(ObjectInputStream.java:808)
at java.io.ObjectInputStream.<init>(ObjectInputStream.java:301)
at
org.apache.spark.serializer.JavaDeserializationStream$$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 have not constructed a test data set for people to play with yet. At this
point I'm hoping someone can try running a job with the same storage level
{code}
StorageLevel(useDisk = true, useMemory = true, useOffHeap = true, deserialized
= false, replication = 2)
{code}
and validate my claim.
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