[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17458866#comment-17458866
]
Mr.黄 edited comment on SPARK-4105 at 12/14/21, 2:39 AM:
[~joshrosen] Hello, I still encountered this problem in the company's project
today, but its error level in the log is WARN. The spark version we used was
2.4.0.cloudera2. I'm not sure this issue has anything to do with Cloudera.
The following is the log information about the error
{code:java}
org.apache.spark.shuffle.FetchFailedException: FAILED_TO_UNCOMPRESS(5)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.throwFetchFailedException(ShuffleBlockFetcherIterator.scala:565)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:470)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:65)
at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:435)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:441)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at
org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:31)
at
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.sort_addToSorter_0$(Unknown
So
urce)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown
Source)
at
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
at
org.apache.spark.sql.execution.UnsafeExternalRowSorter.sort(UnsafeExternalRowSorter.java:216)
at
org.apache.spark.sql.execution.SortExec$$anonfun$1.apply(SortExec.scala:108)
at
org.apache.spark.sql.execution.SortExec$$anonfun$1.apply(SortExec.scala:101)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at
org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1405)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.io.IOException: FAILED_TO_UNCOMPRESS(5)
at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:98)
at org.xerial.snappy.SnappyNative.rawUncompress(Native Method)
at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:474)
at org.xerial.snappy.Snappy.uncompress(Snappy.java:513)
at
org.xerial.snappy.SnappyInputStream.readFully(SnappyInputStream.java:147)
at
org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:99)
at org.xerial.snappy.SnappyInputStream.(SnappyInputStream.java:59)
at
org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:164)
at
org.apache.spark.serializer.SerializerManager.wrapForCompression(SerializerManager.scala:163)
at
org.apache.spark.serializer.SerializerManager.wrapStream(SerializerManager.scala:124)
at
org.apache.spark.shuffle.BlockStoreShuffleReader$$anonfun$3.apply(BlockStoreShuffleReader.scala:50)
at
org.apache.spark.shuffle.BlockStoreShuffleReader$$anonfun$3.apply(BlockStoreShuffleReader.scala:50)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:455)
... 27 more)
{code}
was (Author: JIRAUSER281773):
[~joshrosen] Hello, I still encountered this problem in the company's project
today, but its error level in the log is WARN. The spark version we used was
2.4.0.cloudera2. I'm not sure this issue has anything to do with Cloudera.
The following is the log information about the error
{code:java}
org.apache.spark.shuffle.FetchFailedException: FAILED_TO_UNCOMPRESS(5)
at
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17458866#comment-17458866
]
Mr.黄 edited comment on SPARK-4105 at 12/14/21, 2:38 AM:
[~joshrosen] Hello, I still encountered this problem in the company's project
today, but its error level in the log is WARN. The spark version we used was
2.4.0.cloudera2. I'm not sure this issue has anything to do with Cloudera.
The following is the log information about the error
{code:java}
org.apache.spark.shuffle.FetchFailedException: FAILED_TO_UNCOMPRESS(5)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.throwFetchFailedException(ShuffleBlockFetcherIterator.scala:565)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:470)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:65)
at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:435)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:441)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at
org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:31)
at
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.sort_addToSorter_0$(Unknown
So
urce)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown
Source)
at
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
at
org.apache.spark.sql.execution.UnsafeExternalRowSorter.sort(UnsafeExternalRowSorter.java:216)
at
org.apache.spark.sql.execution.SortExec$$anonfun$1.apply(SortExec.scala:108)
at
org.apache.spark.sql.execution.SortExec$$anonfun$1.apply(SortExec.scala:101)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at
org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1405)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.io.IOException: FAILED_TO_UNCOMPRESS(5)
at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:98)
at org.xerial.snappy.SnappyNative.rawUncompress(Native Method)
at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:474)
at org.xerial.snappy.Snappy.uncompress(Snappy.java:513)
at
org.xerial.snappy.SnappyInputStream.readFully(SnappyInputStream.java:147)
at
org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:99)
at org.xerial.snappy.SnappyInputStream.(SnappyInputStream.java:59)
at
org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:164)
at
org.apache.spark.serializer.SerializerManager.wrapForCompression(SerializerManager.scala:163)
at
org.apache.spark.serializer.SerializerManager.wrapStream(SerializerManager.scala:124)
at
org.apache.spark.shuffle.BlockStoreShuffleReader$$anonfun$3.apply(BlockStoreShuffleReader.scala:50)
at
org.apache.spark.shuffle.BlockStoreShuffleReader$$anonfun$3.apply(BlockStoreShuffleReader.scala:50)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:455)
... 27 more)
{code}
was (Author: JIRAUSER281773):
[~joshrosen] Hello, I still encountered this problem in the company's project
today, but its error level in the log is WARN. The spark version we used was
2.4.0.cloudera2. I'm not sure this issue has anything to do with Cloudera.
The following is the log information about the error
org.apache.spark.shuffle.FetchFailedException: FAILED_TO_UNCOMPRESS(5)
at
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[ https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17283737#comment-17283737 ] Tyson Condie edited comment on SPARK-4105 at 2/12/21, 2:47 PM: --- [~joshrosen] We just experienced a similar stack trace / issue last week in Spark 2.4.4. Could you please share your thoughts on whether it could be related? Job aborted due to stage failure: Task 5639 in stage 87235.0 failed 4 times, most recent failure: Lost task 5639.3 in stage 87235.0 (TID 3466497, executor 82): java.io.IOException: FAILED_TO_UNCOMPRESS(5) at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:98) at org.xerial.snappy.SnappyNative.rawUncompress(Native Method) at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:474) at org.xerial.snappy.Snappy.uncompress(Snappy.java:513) at org.xerial.snappy.SnappyInputStream.hasNextChunk(SnappyInputStream.java:439) at org.xerial.snappy.SnappyInputStream.available(SnappyInputStream.java:486) at org.apache.spark.storage.BufferReleasingInputStream.available(ShuffleBlockFetcherIterator.scala:581) at java.io.BufferedInputStream.read(BufferedInputStream.java:353) at java.io.DataInputStream.read(DataInputStream.java:149) at org.spark_project.guava.io.ByteStreams.read(ByteStreams.java:910) at org.spark_project.guava.io.ByteStreams.readFully(ByteStreams.java:787) at org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$2$$anon$3.next(UnsafeRowSerializer.scala:127) at org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$2$$anon$3.next(UnsafeRowSerializer.scala:110) at scala.collection.Iterator$$anon$12.next(Iterator.scala:445) at scala.collection.Iterator$$anon$11.next(Iterator.scala:410) at org.apache.spark.util.CompletionIterator.next(CompletionIterator.scala:29) at org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:40) at scala.collection.Iterator$$anon$11.next(Iterator.scala:410) at org.apache.spark.sql.execution.columnar.CachedRDDBuilder$$anonfun$1$$anon$1.next(InMemoryRelation.scala:94) at org.apache.spark.sql.execution.columnar.CachedRDDBuilder$$anonfun$1$$anon$1.next(InMemoryRelation.scala:84) at org.apache.spark.storage.memory.MemoryStore.putIterator(MemoryStore.scala:222) at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:299) at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1165) at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1156) at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:1091) at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1156) at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:882) at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:335) at org.apache.spark.rdd.RDD.iterator(RDD.scala:286) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324) at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324) at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324) at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324) at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55) at org.apache.spark.scheduler.Task.run(Task.scala:123) at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) was (Author: tcondie): [~joshrosen] We just experienced a similar stack trace / issue last week. Could you please share you thoughts on whether it could be related? Job aborted due to stage failure: Task
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16790258#comment-16790258
]
feiwang edited comment on SPARK-4105 at 3/12/19 6:03 AM:
-
Is there anyone see these errors in latest version, such as spark-2.3?
Can anyone provide a reproducible case?
was (Author: feiwang):
Is there anyone see these errors in latest version such as spark-2.3?
Can anyone provide a reproducible case?
> FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based
> shuffle
> -
>
> Key: SPARK-4105
> URL: https://issues.apache.org/jira/browse/SPARK-4105
> Project: Spark
> Issue Type: Bug
> Components: Shuffle, Spark Core
>Affects Versions: 1.2.0, 1.2.1, 1.3.0, 1.4.1, 1.5.1, 1.6.1, 2.0.0
>Reporter: Josh Rosen
>Assignee: Davies Liu
>Priority: Critical
> Fix For: 2.2.0
>
> Attachments: JavaObjectToSerialize.java,
> SparkFailedToUncompressGenerator.scala
>
>
> We have seen non-deterministic {{FAILED_TO_UNCOMPRESS(5)}} errors during
> shuffle read. Here's a sample stacktrace from an executor:
> {code}
> 14/10/23 18:34:11 ERROR Executor: Exception in task 1747.3 in stage 11.0 (TID
> 33053)
> java.io.IOException: FAILED_TO_UNCOMPRESS(5)
> at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:78)
> at org.xerial.snappy.SnappyNative.rawUncompress(Native Method)
> at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:391)
> at org.xerial.snappy.Snappy.uncompress(Snappy.java:427)
> at
> org.xerial.snappy.SnappyInputStream.readFully(SnappyInputStream.java:127)
> at
> org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:88)
> at org.xerial.snappy.SnappyInputStream.(SnappyInputStream.java:58)
> at
> org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:128)
> at
> org.apache.spark.storage.BlockManager.wrapForCompression(BlockManager.scala:1090)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:116)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:115)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:243)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:52)
> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
> at
> org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:30)
> at
> org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> at
> org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:129)
> at
> org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:159)
> at
> org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:158)
> at
> scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> at
> scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)
> at org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:158)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> at
> org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> at
> org.apache.spark.rdd.FlatMappedValuesRDD.compute(FlatMappedValuesRDD.scala:31)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> at org.apache.spark.scheduler.Task.run(Task.scala:56)
> at
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15535884#comment-15535884
]
Luca Menichetti edited comment on SPARK-4105 at 9/30/16 2:25 PM:
-
I have exactly the same problem. It occurs when I process more than one TB of
data.
Splitting the computation in halves it works perfectly, but I need to process
the whole thing together.
{noformat}
java.io.IOException: failed to read chunk
at
org.xerial.snappy.SnappyInputStream.hasNextChunk(SnappyInputStream.java:347)
at
org.xerial.snappy.SnappyInputStream.rawRead(SnappyInputStream.java:158)
at org.xerial.snappy.SnappyInputStream.read(SnappyInputStream.java:142)
at java.io.BufferedInputStream.fill(BufferedInputStream.java:235)
at java.io.BufferedInputStream.read1(BufferedInputStream.java:275)
at java.io.BufferedInputStream.read(BufferedInputStream.java:334)
at java.io.DataInputStream.read(DataInputStream.java:149)
at org.spark-project.guava.io.ByteStreams.read(ByteStreams.java:899)
at
org.spark-project.guava.io.ByteStreams.readFully(ByteStreams.java:733)
at
org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3$$anon$1.next(UnsafeRowSerializer.scala:119)
at
org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3$$anon$1.next(UnsafeRowSerializer.scala:102)
at scala.collection.Iterator$$anon$13.next(Iterator.scala:372)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at
org.apache.spark.util.CompletionIterator.next(CompletionIterator.scala:30)
at
org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at
org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.processInputs(TungstenAggregationIterator.scala:512)
at
org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.(TungstenAggregationIterator.scala:686)
at
org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:95)
at
org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:86)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
{noformat}
as a consequence, since I am using SparkSQL, the job will fail, reporting
{noformat}
16/09/30 14:40:51 ERROR LiveListenerBus: Listener SQLListener threw an exception
java.lang.NullPointerException
at
org.apache.spark.sql.execution.ui.SQLListener.onTaskEnd(SQLListener.scala:167)
at
org.apache.spark.scheduler.SparkListenerBus$class.onPostEvent(SparkListenerBus.scala:42)
at
org.apache.spark.scheduler.LiveListenerBus.onPostEvent(LiveListenerBus.scala:31)
at
org.apache.spark.scheduler.LiveListenerBus.onPostEvent(LiveListenerBus.scala:31)
at
org.apache.spark.util.ListenerBus$class.postToAll(ListenerBus.scala:55)
at
org.apache.spark.util.AsynchronousListenerBus.postToAll(AsynchronousListenerBus.scala:37)
at
org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(AsynchronousListenerBus.scala:80)
at
org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(AsynchronousListenerBus.scala:65)
at
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15535884#comment-15535884
]
Luca Menichetti edited comment on SPARK-4105 at 9/30/16 2:04 PM:
-
I have exactly the same problem. It occurs when I process more than one TB of
data.
Splitting the computation in halves it works perfectly, but I need to process
the whole thing together.
{noformat}
java.io.IOException: failed to read chunk
at
org.xerial.snappy.SnappyInputStream.hasNextChunk(SnappyInputStream.java:347)
at
org.xerial.snappy.SnappyInputStream.rawRead(SnappyInputStream.java:158)
at org.xerial.snappy.SnappyInputStream.read(SnappyInputStream.java:142)
at java.io.BufferedInputStream.fill(BufferedInputStream.java:235)
at java.io.BufferedInputStream.read1(BufferedInputStream.java:275)
at java.io.BufferedInputStream.read(BufferedInputStream.java:334)
at java.io.DataInputStream.read(DataInputStream.java:149)
at org.spark-project.guava.io.ByteStreams.read(ByteStreams.java:899)
at
org.spark-project.guava.io.ByteStreams.readFully(ByteStreams.java:733)
at
org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3$$anon$1.next(UnsafeRowSerializer.scala:119)
at
org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3$$anon$1.next(UnsafeRowSerializer.scala:102)
at scala.collection.Iterator$$anon$13.next(Iterator.scala:372)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at
org.apache.spark.util.CompletionIterator.next(CompletionIterator.scala:30)
at
org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at
org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.processInputs(TungstenAggregationIterator.scala:512)
at
org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.(TungstenAggregationIterator.scala:686)
at
org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:95)
at
org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:86)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
{noformat}
as a consequence, since I am using SparkSQL, the job will fail, reporting
{noformat}
16/09/30 14:40:51 ERROR LiveListenerBus: Listener SQLListener threw an exception
java.lang.NullPointerException
at
org.apache.spark.sql.execution.ui.SQLListener.onTaskEnd(SQLListener.scala:167)
at
org.apache.spark.scheduler.SparkListenerBus$class.onPostEvent(SparkListenerBus.scala:42)
at
org.apache.spark.scheduler.LiveListenerBus.onPostEvent(LiveListenerBus.scala:31)
at
org.apache.spark.scheduler.LiveListenerBus.onPostEvent(LiveListenerBus.scala:31)
at
org.apache.spark.util.ListenerBus$class.postToAll(ListenerBus.scala:55)
at
org.apache.spark.util.AsynchronousListenerBus.postToAll(AsynchronousListenerBus.scala:37)
at
org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(AsynchronousListenerBus.scala:80)
at
org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(AsynchronousListenerBus.scala:65)
at
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15535884#comment-15535884
]
Luca Menichetti edited comment on SPARK-4105 at 9/30/16 1:54 PM:
-
I have exactly the same problem. It occurs when I process more than one TB of
data.
Splitting the computation in halves it works perfectly, but I need to process
the whole thing together.
{noformat}
java.io.IOException: failed to read chunk
at
org.xerial.snappy.SnappyInputStream.hasNextChunk(SnappyInputStream.java:347)
at
org.xerial.snappy.SnappyInputStream.rawRead(SnappyInputStream.java:158)
at org.xerial.snappy.SnappyInputStream.read(SnappyInputStream.java:142)
at java.io.BufferedInputStream.fill(BufferedInputStream.java:235)
at java.io.BufferedInputStream.read1(BufferedInputStream.java:275)
at java.io.BufferedInputStream.read(BufferedInputStream.java:334)
at java.io.DataInputStream.read(DataInputStream.java:149)
at org.spark-project.guava.io.ByteStreams.read(ByteStreams.java:899)
at
org.spark-project.guava.io.ByteStreams.readFully(ByteStreams.java:733)
at
org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3$$anon$1.next(UnsafeRowSerializer.scala:119)
at
org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3$$anon$1.next(UnsafeRowSerializer.scala:102)
at scala.collection.Iterator$$anon$13.next(Iterator.scala:372)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at
org.apache.spark.util.CompletionIterator.next(CompletionIterator.scala:30)
at
org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at
org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.processInputs(TungstenAggregationIterator.scala:512)
at
org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.(TungstenAggregationIterator.scala:686)
at
org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:95)
at
org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:86)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
{noformat}
as a consequence, since I am using SparkSQL, the job will fail, reporting
{noformat}
16/09/30 14:40:51 ERROR LiveListenerBus: Listener SQLListener threw an exception
java.lang.NullPointerException
at
org.apache.spark.sql.execution.ui.SQLListener.onTaskEnd(SQLListener.scala:167)
at
org.apache.spark.scheduler.SparkListenerBus$class.onPostEvent(SparkListenerBus.scala:42)
at
org.apache.spark.scheduler.LiveListenerBus.onPostEvent(LiveListenerBus.scala:31)
at
org.apache.spark.scheduler.LiveListenerBus.onPostEvent(LiveListenerBus.scala:31)
at
org.apache.spark.util.ListenerBus$class.postToAll(ListenerBus.scala:55)
at
org.apache.spark.util.AsynchronousListenerBus.postToAll(AsynchronousListenerBus.scala:37)
at
org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(AsynchronousListenerBus.scala:80)
at
org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(AsynchronousListenerBus.scala:65)
at
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15535884#comment-15535884
]
Luca Menichetti edited comment on SPARK-4105 at 9/30/16 12:43 PM:
--
I have exactly the same problem. It occurs when I process more than one TB of
data.
Splitting the computation in halves it works perfectly, but I need to process
the whole thing together.
{noformat}
java.io.IOException: failed to read chunk
at
org.xerial.snappy.SnappyInputStream.hasNextChunk(SnappyInputStream.java:347)
at
org.xerial.snappy.SnappyInputStream.rawRead(SnappyInputStream.java:158)
at org.xerial.snappy.SnappyInputStream.read(SnappyInputStream.java:142)
at java.io.BufferedInputStream.fill(BufferedInputStream.java:235)
at java.io.BufferedInputStream.read1(BufferedInputStream.java:275)
at java.io.BufferedInputStream.read(BufferedInputStream.java:334)
at java.io.DataInputStream.read(DataInputStream.java:149)
at org.spark-project.guava.io.ByteStreams.read(ByteStreams.java:899)
at
org.spark-project.guava.io.ByteStreams.readFully(ByteStreams.java:733)
at
org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3$$anon$1.next(UnsafeRowSerializer.scala:119)
at
org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3$$anon$1.next(UnsafeRowSerializer.scala:102)
at scala.collection.Iterator$$anon$13.next(Iterator.scala:372)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at
org.apache.spark.util.CompletionIterator.next(CompletionIterator.scala:30)
at
org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at
org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.processInputs(TungstenAggregationIterator.scala:512)
at
org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.(TungstenAggregationIterator.scala:686)
at
org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:95)
at
org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:86)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
{noformat}
as a consequence, since I am using SparkSQL, the job will fail, reporting
{noformat}
16/09/30 14:40:51 ERROR LiveListenerBus: Listener SQLListener threw an exception
java.lang.NullPointerException
at
org.apache.spark.sql.execution.ui.SQLListener.onTaskEnd(SQLListener.scala:167)
at
org.apache.spark.scheduler.SparkListenerBus$class.onPostEvent(SparkListenerBus.scala:42)
at
org.apache.spark.scheduler.LiveListenerBus.onPostEvent(LiveListenerBus.scala:31)
at
org.apache.spark.scheduler.LiveListenerBus.onPostEvent(LiveListenerBus.scala:31)
at
org.apache.spark.util.ListenerBus$class.postToAll(ListenerBus.scala:55)
at
org.apache.spark.util.AsynchronousListenerBus.postToAll(AsynchronousListenerBus.scala:37)
at
org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(AsynchronousListenerBus.scala:80)
at
org.apache.spark.util.AsynchronousListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(AsynchronousListenerBus.scala:65)
at
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15453034#comment-15453034
]
Matthew Seal edited comment on SPARK-4105 at 8/31/16 9:42 PM:
--
Producible on 1.6.1 from a count() call after cache(df.repartition(2000))
against a dataset built from a PySpark generated RDD once I grew it into GB
sizes (a few million rows). Smaller dataset sizes have consistently not
triggered this issue.
{code}
java.io.IOException: failed to uncompress the chunk: PARSING_ERROR(2)
at
org.xerial.snappy.SnappyInputStream.hasNextChunk(SnappyInputStream.java:361)
at
org.xerial.snappy.SnappyInputStream.rawRead(SnappyInputStream.java:158)
at org.xerial.snappy.SnappyInputStream.read(SnappyInputStream.java:142)
at java.io.BufferedInputStream.fill(BufferedInputStream.java:246)
at java.io.BufferedInputStream.read1(BufferedInputStream.java:286)
at java.io.BufferedInputStream.read(BufferedInputStream.java:345)
at java.io.DataInputStream.read(DataInputStream.java:149)
at org.spark-project.guava.io.ByteStreams.read(ByteStreams.java:899)
at
org.spark-project.guava.io.ByteStreams.readFully(ByteStreams.java:733)
at
org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3$$anon$1.next(UnsafeRowSerializer.scala:119)
at
org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3$$anon$1.next(UnsafeRowSerializer.scala:102)
at scala.collection.Iterator$$anon$13.next(Iterator.scala:372)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at
org.apache.spark.util.CompletionIterator.next(CompletionIterator.scala:30)
at
org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at
org.apache.spark.sql.execution.UnsafeExternalRowSorter.sort(UnsafeExternalRowSorter.java:168)
at org.apache.spark.sql.execution.Sort$$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.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:88)
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.api.python.PythonRDD.compute(PythonRDD.scala:70)
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.rdd.UnionRDD.compute(UnionRDD.scala:87)
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)
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15453034#comment-15453034
]
Matthew Seal edited comment on SPARK-4105 at 8/31/16 6:50 PM:
--
Producible on 1.6.1 from a count() call against a dataset built from a PySpark
generated RDD once I grew it into GB sizes (a few million rows). Smaller
dataset sizes have consistently not triggered this issue.
{code}
java.io.IOException: failed to uncompress the chunk: PARSING_ERROR(2)
at
org.xerial.snappy.SnappyInputStream.hasNextChunk(SnappyInputStream.java:361)
at
org.xerial.snappy.SnappyInputStream.rawRead(SnappyInputStream.java:158)
at org.xerial.snappy.SnappyInputStream.read(SnappyInputStream.java:142)
at java.io.BufferedInputStream.fill(BufferedInputStream.java:246)
at java.io.BufferedInputStream.read1(BufferedInputStream.java:286)
at java.io.BufferedInputStream.read(BufferedInputStream.java:345)
at java.io.DataInputStream.read(DataInputStream.java:149)
at org.spark-project.guava.io.ByteStreams.read(ByteStreams.java:899)
at
org.spark-project.guava.io.ByteStreams.readFully(ByteStreams.java:733)
at
org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3$$anon$1.next(UnsafeRowSerializer.scala:119)
at
org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3$$anon$1.next(UnsafeRowSerializer.scala:102)
at scala.collection.Iterator$$anon$13.next(Iterator.scala:372)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at
org.apache.spark.util.CompletionIterator.next(CompletionIterator.scala:30)
at
org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
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.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:88)
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.api.python.PythonRDD.compute(PythonRDD.scala:70)
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.rdd.UnionRDD.compute(UnionRDD.scala:87)
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
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15251201#comment-15251201
]
Zhongshuai Pei edited comment on SPARK-4105 at 4/21/16 8:08 AM:
I find that some task recompute before this problem happened,and I think that
retry operation Corrupted shuffle file that caused this problem. I debug the
code and corrupted the shuffle file before it has been readed, this problem
happened every time.maybe we can regenerate the shuffle file when it is
corrupted
code like this
BlockStoreShuffleReader.scala
{code:title=Bar.java|borderStyle=solid}
val wrappedStreams = blockFetcherItr.map { case (blockId, inputStream) =>
- serializerManager.wrapForCompression(blockId, inputStream)
+ try {
+serializerManager.wrapForCompression(blockId, inputStream)
+ } catch {
+case e: IOException => {
+ if ((e.getMessage.contains("FAILED_TO_UNCOMPRESS(5)") ||
+ e.getMessage.contains("PARSING_ERROR(2)") ||
+ e.getMessage.contains("Stream is corrupted")) &&
blockId.isShuffle) {
+val shuffleBlockId = blockId.asInstanceOf[ShuffleBlockId]
+throw new FetchFailedException(
+ blockManager.blockManagerId, shuffleBlockId.shuffleId,
+ shuffleBlockId.mapId, shuffleBlockId.reduceId, e)
+ } else {
+throw e
+ }
+}
+ }
}
{code}
[~joshrosen] [~wy90021] [~vgirijira][~alpinegizmo]
was (Author: doingdone9):
I find that some task recompute before this problem happened,and I think that
retry operation Corrupted shuffle file that caused this problem. I debug the
code and corrupted the shuffle file before it has been readed, this problem
happened every time.maybe we can regenerate the shuffle file when it is
corrupted
code like this
BlockStoreShuffleReader.scala
{code:title=Bar.java|borderStyle=solid}
val wrappedStreams = blockFetcherItr.map { case (blockId, inputStream) =>
- serializerManager.wrapForCompression(blockId, inputStream)
+ try {
+serializerManager.wrapForCompression(blockId, inputStream)
+ } catch {
+case e: IOException => {
+ if ((e.getMessage.contains("FAILED_TO_UNCOMPRESS(5)") ||
+ e.getMessage.contains("PARSING_ERROR(2)") ||
+ e.getMessage.contains("Stream is corrupted")) &&
blockId.isShuffle) {
+val shuffleBlockId = blockId.asInstanceOf[ShuffleBlockId]
+throw new FetchFailedException(
+ blockManager.blockManagerId, shuffleBlockId.shuffleId,
+ shuffleBlockId.mapId, shuffleBlockId.reduceId, e)
+ } else {
+throw e
+ }
+}
+ }
}
{code}
> FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based
> shuffle
> -
>
> Key: SPARK-4105
> URL: https://issues.apache.org/jira/browse/SPARK-4105
> Project: Spark
> Issue Type: Bug
> Components: Shuffle, Spark Core
>Affects Versions: 1.2.0, 1.2.1, 1.3.0, 1.4.1
>Reporter: Josh Rosen
>Assignee: Josh Rosen
>Priority: Critical
> Attachments: JavaObjectToSerialize.java,
> SparkFailedToUncompressGenerator.scala
>
>
> We have seen non-deterministic {{FAILED_TO_UNCOMPRESS(5)}} errors during
> shuffle read. Here's a sample stacktrace from an executor:
> {code}
> 14/10/23 18:34:11 ERROR Executor: Exception in task 1747.3 in stage 11.0 (TID
> 33053)
> java.io.IOException: FAILED_TO_UNCOMPRESS(5)
> at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:78)
> at org.xerial.snappy.SnappyNative.rawUncompress(Native Method)
> at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:391)
> at org.xerial.snappy.Snappy.uncompress(Snappy.java:427)
> at
> org.xerial.snappy.SnappyInputStream.readFully(SnappyInputStream.java:127)
> at
> org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:88)
> at org.xerial.snappy.SnappyInputStream.(SnappyInputStream.java:58)
> at
> org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:128)
> at
> org.apache.spark.storage.BlockManager.wrapForCompression(BlockManager.scala:1090)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:116)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:115)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:243)
> at
>
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15251201#comment-15251201
]
Zhongshuai Pei edited comment on SPARK-4105 at 4/21/16 3:51 AM:
I find that some task recompute before this problem happened,and I think that
retry operation Corrupted shuffle file that caused this problem. I debug the
code and corrupted the shuffle file before it has been readed, this problem
happened every time.maybe we can regenerate the shuffle file when it is
corrupted
code like this
BlockStoreShuffleReader.scala
{code:title=Bar.java|borderStyle=solid}
val wrappedStreams = blockFetcherItr.map { case (blockId, inputStream) =>
- serializerManager.wrapForCompression(blockId, inputStream)
+ try {
+serializerManager.wrapForCompression(blockId, inputStream)
+ } catch {
+case e: IOException => {
+ if ((e.getMessage.contains("FAILED_TO_UNCOMPRESS(5)") ||
+ e.getMessage.contains("PARSING_ERROR(2)") ||
+ e.getMessage.contains("Stream is corrupted")) &&
blockId.isShuffle) {
+val shuffleBlockId = blockId.asInstanceOf[ShuffleBlockId]
+throw new FetchFailedException(
+ blockManager.blockManagerId, shuffleBlockId.shuffleId,
+ shuffleBlockId.mapId, shuffleBlockId.reduceId, e)
+ } else {
+throw e
+ }
+}
+ }
}
{code}
was (Author: doingdone9):
I find that some task recompute before this problem happened,and I think that
retry operation Corrupted shuffle file that caused this problem. I debug the
code and corrupted the shuffle file before it has been readed, this problem
happened every time.maybe we can regenerate the shuffle file when it is
corrupted
code like this
BlockStoreShuffleReader.scala
{quote}
val wrappedStreams = blockFetcherItr.map { case (blockId, inputStream) =>
- serializerManager.wrapForCompression(blockId, inputStream)
+ try {
+serializerManager.wrapForCompression(blockId, inputStream)
+ } catch {
+case e: IOException => {
+ if ((e.getMessage.contains("FAILED_TO_UNCOMPRESS(5)") ||
+ e.getMessage.contains("PARSING_ERROR(2)") ||
+ e.getMessage.contains("Stream is corrupted")) &&
blockId.isShuffle) {
+val shuffleBlockId = blockId.asInstanceOf[ShuffleBlockId]
+throw new FetchFailedException(
+ blockManager.blockManagerId, shuffleBlockId.shuffleId,
+ shuffleBlockId.mapId, shuffleBlockId.reduceId, e)
+ } else {
+throw e
+ }
+}
+ }
}
{quote}
> FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based
> shuffle
> -
>
> Key: SPARK-4105
> URL: https://issues.apache.org/jira/browse/SPARK-4105
> Project: Spark
> Issue Type: Bug
> Components: Shuffle, Spark Core
>Affects Versions: 1.2.0, 1.2.1, 1.3.0, 1.4.1
>Reporter: Josh Rosen
>Assignee: Josh Rosen
>Priority: Critical
> Attachments: JavaObjectToSerialize.java,
> SparkFailedToUncompressGenerator.scala
>
>
> We have seen non-deterministic {{FAILED_TO_UNCOMPRESS(5)}} errors during
> shuffle read. Here's a sample stacktrace from an executor:
> {code}
> 14/10/23 18:34:11 ERROR Executor: Exception in task 1747.3 in stage 11.0 (TID
> 33053)
> java.io.IOException: FAILED_TO_UNCOMPRESS(5)
> at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:78)
> at org.xerial.snappy.SnappyNative.rawUncompress(Native Method)
> at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:391)
> at org.xerial.snappy.Snappy.uncompress(Snappy.java:427)
> at
> org.xerial.snappy.SnappyInputStream.readFully(SnappyInputStream.java:127)
> at
> org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:88)
> at org.xerial.snappy.SnappyInputStream.(SnappyInputStream.java:58)
> at
> org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:128)
> at
> org.apache.spark.storage.BlockManager.wrapForCompression(BlockManager.scala:1090)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:116)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:115)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:243)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:52)
> at
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15251201#comment-15251201
]
Zhongshuai Pei edited comment on SPARK-4105 at 4/21/16 3:49 AM:
I find that some task recompute before this problem happened,and I think that
retry operation Corrupted shuffle file that caused this problem. I debug the
code and corrupted the shuffle file before it has been readed, this problem
happened every time.maybe we can regenerate the shuffle file when it is
corrupted
code like this
BlockStoreShuffleReader.scala
{quote}
val wrappedStreams = blockFetcherItr.map { case (blockId, inputStream) =>
- serializerManager.wrapForCompression(blockId, inputStream)
+ try {
+serializerManager.wrapForCompression(blockId, inputStream)
+ } catch {
+case e: IOException => {
+ if ((e.getMessage.contains("FAILED_TO_UNCOMPRESS(5)") ||
+ e.getMessage.contains("PARSING_ERROR(2)") ||
+ e.getMessage.contains("Stream is corrupted")) &&
blockId.isShuffle) {
+val shuffleBlockId = blockId.asInstanceOf[ShuffleBlockId]
+throw new FetchFailedException(
+ blockManager.blockManagerId, shuffleBlockId.shuffleId,
+ shuffleBlockId.mapId, shuffleBlockId.reduceId, e)
+ } else {
+throw e
+ }
+}
+ }
}
{quote}
was (Author: doingdone9):
I find that some task recompute before this problem happened,and I think that
retry operation Corrupted shuffle file that caused this problem. I debug the
code and corrupted the shuffle file before it has been readed, this problem
happened every time.maybe we can regenerate the shuffle file when it is
corrupted
code like this
BlockStoreShuffleReader.scala
```
val wrappedStreams = blockFetcherItr.map { case (blockId, inputStream) =>
- serializerManager.wrapForCompression(blockId, inputStream)
+ try {
+serializerManager.wrapForCompression(blockId, inputStream)
+ } catch {
+case e: IOException => {
+ if ((e.getMessage.contains("FAILED_TO_UNCOMPRESS(5)") ||
+ e.getMessage.contains("PARSING_ERROR(2)") ||
+ e.getMessage.contains("Stream is corrupted")) &&
blockId.isShuffle) {
+val shuffleBlockId = blockId.asInstanceOf[ShuffleBlockId]
+throw new FetchFailedException(
+ blockManager.blockManagerId, shuffleBlockId.shuffleId,
+ shuffleBlockId.mapId, shuffleBlockId.reduceId, e)
+ } else {
+throw e
+ }
+}
+ }
}
```
> FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based
> shuffle
> -
>
> Key: SPARK-4105
> URL: https://issues.apache.org/jira/browse/SPARK-4105
> Project: Spark
> Issue Type: Bug
> Components: Shuffle, Spark Core
>Affects Versions: 1.2.0, 1.2.1, 1.3.0, 1.4.1
>Reporter: Josh Rosen
>Assignee: Josh Rosen
>Priority: Critical
> Attachments: JavaObjectToSerialize.java,
> SparkFailedToUncompressGenerator.scala
>
>
> We have seen non-deterministic {{FAILED_TO_UNCOMPRESS(5)}} errors during
> shuffle read. Here's a sample stacktrace from an executor:
> {code}
> 14/10/23 18:34:11 ERROR Executor: Exception in task 1747.3 in stage 11.0 (TID
> 33053)
> java.io.IOException: FAILED_TO_UNCOMPRESS(5)
> at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:78)
> at org.xerial.snappy.SnappyNative.rawUncompress(Native Method)
> at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:391)
> at org.xerial.snappy.Snappy.uncompress(Snappy.java:427)
> at
> org.xerial.snappy.SnappyInputStream.readFully(SnappyInputStream.java:127)
> at
> org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:88)
> at org.xerial.snappy.SnappyInputStream.(SnappyInputStream.java:58)
> at
> org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:128)
> at
> org.apache.spark.storage.BlockManager.wrapForCompression(BlockManager.scala:1090)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:116)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:115)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:243)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:52)
> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
> at
>
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15214712#comment-15214712
]
Giri R Varatharajan edited comment on SPARK-4105 at 3/28/16 7:08 PM:
-
This is the blocker in Spark 1.5.2 also. I tried using LZ4 or LZF. But I got
different exception.
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure:
Task 1 in stage 8.1 failed 4 times, most recent failure: Lost task 1.3 in stage
8.1 (TID 2741, p01bdl870.aap.csaa.com): java.io.IOException: Stream is corrupted
was (Author: vgirijira):
This is the blocker in Spark 1.5.2 also.
> FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based
> shuffle
> -
>
> Key: SPARK-4105
> URL: https://issues.apache.org/jira/browse/SPARK-4105
> Project: Spark
> Issue Type: Bug
> Components: Shuffle, Spark Core
>Affects Versions: 1.2.0, 1.2.1, 1.3.0, 1.4.1
>Reporter: Josh Rosen
>Assignee: Josh Rosen
>Priority: Blocker
> Attachments: JavaObjectToSerialize.java,
> SparkFailedToUncompressGenerator.scala
>
>
> We have seen non-deterministic {{FAILED_TO_UNCOMPRESS(5)}} errors during
> shuffle read. Here's a sample stacktrace from an executor:
> {code}
> 14/10/23 18:34:11 ERROR Executor: Exception in task 1747.3 in stage 11.0 (TID
> 33053)
> java.io.IOException: FAILED_TO_UNCOMPRESS(5)
> at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:78)
> at org.xerial.snappy.SnappyNative.rawUncompress(Native Method)
> at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:391)
> at org.xerial.snappy.Snappy.uncompress(Snappy.java:427)
> at
> org.xerial.snappy.SnappyInputStream.readFully(SnappyInputStream.java:127)
> at
> org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:88)
> at org.xerial.snappy.SnappyInputStream.(SnappyInputStream.java:58)
> at
> org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:128)
> at
> org.apache.spark.storage.BlockManager.wrapForCompression(BlockManager.scala:1090)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:116)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:115)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:243)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:52)
> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
> at
> org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:30)
> at
> org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> at
> org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:129)
> at
> org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:159)
> at
> org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:158)
> at
> scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> at
> scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)
> at org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:158)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> at
> org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> at
> org.apache.spark.rdd.FlatMappedValuesRDD.compute(FlatMappedValuesRDD.scala:31)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
> at
>
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15081244#comment-15081244
]
Renu Yadav edited comment on SPARK-4105 at 1/4/16 3:07 PM:
---
Hi ,
I am also getting similar exception on spark 1.4.1 when shuffling above 500GB
data,but this exception occurs sometimes
Please help.
was (Author: renuyadav):
Hi ,
I am also getting similar exception on spark 1.4.1 when shuffling above 500GB
data,but this exception occurs something
Please help.
> FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based
> shuffle
> -
>
> Key: SPARK-4105
> URL: https://issues.apache.org/jira/browse/SPARK-4105
> Project: Spark
> Issue Type: Bug
> Components: Shuffle, Spark Core
>Affects Versions: 1.2.0, 1.2.1, 1.3.0, 1.4.1
>Reporter: Josh Rosen
>Assignee: Josh Rosen
>Priority: Blocker
> Attachments: JavaObjectToSerialize.java,
> SparkFailedToUncompressGenerator.scala
>
>
> We have seen non-deterministic {{FAILED_TO_UNCOMPRESS(5)}} errors during
> shuffle read. Here's a sample stacktrace from an executor:
> {code}
> 14/10/23 18:34:11 ERROR Executor: Exception in task 1747.3 in stage 11.0 (TID
> 33053)
> java.io.IOException: FAILED_TO_UNCOMPRESS(5)
> at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:78)
> at org.xerial.snappy.SnappyNative.rawUncompress(Native Method)
> at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:391)
> at org.xerial.snappy.Snappy.uncompress(Snappy.java:427)
> at
> org.xerial.snappy.SnappyInputStream.readFully(SnappyInputStream.java:127)
> at
> org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:88)
> at org.xerial.snappy.SnappyInputStream.(SnappyInputStream.java:58)
> at
> org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:128)
> at
> org.apache.spark.storage.BlockManager.wrapForCompression(BlockManager.scala:1090)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:116)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:115)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:243)
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:52)
> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
> at
> org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:30)
> at
> org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> at
> org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:129)
> at
> org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:159)
> at
> org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:158)
> at
> scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> at
> scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)
> at org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:158)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> at
> org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> at
> org.apache.spark.rdd.FlatMappedValuesRDD.compute(FlatMappedValuesRDD.scala:31)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> at org.apache.spark.scheduler.Task.run(Task.scala:56)
> at
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[ https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14536880#comment-14536880 ] Allan Douglas R. de Oliveira edited comment on SPARK-4105 at 5/9/15 8:36 PM: - Got something like this but using: - Java serializer - Snappy - Also Lz4 - Spark 1.3.0 (most things on default settings) We got the error many times on the same cluster (which was doing fine for days) but after recreating it the problem disappeared again. Stack traces (the first two are from two different runs using Snappy and the third from an execution using Lz4): 15/05/09 13:05:55 ERROR Executor: Exception in task 234.2 in stage 27.1 (TID 876507) java.io.IOException: PARSING_ERROR(2) at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:84) at org.xerial.snappy.SnappyNative.uncompressedLength(Native Method) at org.xerial.snappy.Snappy.uncompressedLength(Snappy.java:594) at org.xerial.snappy.SnappyInputStream.hasNextChunk(SnappyInputStream.java:358) at org.xerial.snappy.SnappyInputStream.read(SnappyInputStream.java:387) at java.io.ObjectInputStream$PeekInputStream.peek(ObjectInputStream.java:2293) at java.io.ObjectInputStream$BlockDataInputStream.peek(ObjectInputStream.java:2586) at java.io.ObjectInputStream$BlockDataInputStream.peekByte(ObjectInputStream.java:2596) at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1318) at java.io.ObjectInputStream.readObject(ObjectInputStream.java:370) at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:68) at org.apache.spark.serializer.DeserializationStream$$anon$1.getNext(Serializer.scala:133) at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:71) at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32) at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371) at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32) at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39) at org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:125) at org.apache.spark.Aggregator.combineValuesByKey(Aggregator.scala:60) at org.apache.spark.shuffle.hash.HashShuffleReader.read(HashShuffleReader.scala:46) at org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:92) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) at org.apache.spark.scheduler.Task.run(Task.scala:64) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:203) 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) 15/05/08 20:46:28 WARN scheduler.TaskSetManager: Lost task 9559.0 in stage 55.0 (TID 424644, ip-172-24-36-214.ec2.internal): java.io.IOException: FAILED_TO_UNCOMPRESS(5) at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:84) at org.xerial.snappy.SnappyNative.rawUncompress(Native Method) at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:444) at org.xerial.snappy.Snappy.uncompress(Snappy.java:480) at org.xerial.snappy.SnappyInputStream.hasNextChunk(SnappyInputStream.java:362) at org.xerial.snappy.SnappyInputStream.read(SnappyInputStream.java:387) at java.io.ObjectInputStream$PeekInputStream.peek(ObjectInputStream.java:2293) at java.io.ObjectInputStream$BlockDataInputStream.peek(ObjectInputStream.java:2586) at
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14532692#comment-14532692
]
Guillaume E.B. edited comment on SPARK-4105 at 5/7/15 2:10 PM:
---
I have been able to reproduce this bug several times on my cluster composed of
4 VMs (6 proc, 32GB ram) using the classes I join. These classes are based on
my business use case on which I first ran into the issue and the example given
on this page, it is basically joining two flows with massive spilling on disk.
I reproduce the bug starting from 8000 mappers generating each 10 objects
(first two arguments). This is equivalent to about 36GB of spilled data. With
2000 mappers, the bug did not happen. This issue occurs clearly for me when a
massive volume of data is involved.
was (Author: geynard):
I have been able to reproduce this bug several times on my cluster composed of
4 VMs (6 proc, 32GB ram) using the classes I join. These classes are based on
my business use case on which I first ran into the issue and the example given
on this page, it is basically joining two flows with massive spilling on disk.
I reproduce the bug starting from 8000 mappers generating each 10 object
(first two arguments). This is equivalent to about 36GB of spilled data. With
2000 mappers, the bug did not happen. This issue occurs clearly for me when a
massive volume of data is involved.
FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based
shuffle
-
Key: SPARK-4105
URL: https://issues.apache.org/jira/browse/SPARK-4105
Project: Spark
Issue Type: Bug
Components: Shuffle, Spark Core
Affects Versions: 1.2.0, 1.2.1, 1.3.0
Reporter: Josh Rosen
Assignee: Josh Rosen
Priority: Blocker
Attachments: JavaObjectToSerialize.java,
SparkFailedToUncompressGenerator.scala
We have seen non-deterministic {{FAILED_TO_UNCOMPRESS(5)}} errors during
shuffle read. Here's a sample stacktrace from an executor:
{code}
14/10/23 18:34:11 ERROR Executor: Exception in task 1747.3 in stage 11.0 (TID
33053)
java.io.IOException: FAILED_TO_UNCOMPRESS(5)
at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:78)
at org.xerial.snappy.SnappyNative.rawUncompress(Native Method)
at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:391)
at org.xerial.snappy.Snappy.uncompress(Snappy.java:427)
at
org.xerial.snappy.SnappyInputStream.readFully(SnappyInputStream.java:127)
at
org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:88)
at org.xerial.snappy.SnappyInputStream.init(SnappyInputStream.java:58)
at
org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:128)
at
org.apache.spark.storage.BlockManager.wrapForCompression(BlockManager.scala:1090)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:116)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:115)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:243)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:52)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at
org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:30)
at
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at
org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:129)
at
org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:159)
at
org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:158)
at
scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at
scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)
at org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:158)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at
org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31)
at
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14532692#comment-14532692
]
Guillaume E.B. edited comment on SPARK-4105 at 5/7/15 2:13 PM:
---
I have been able to reproduce this bug several times on my cluster composed of
4 VMs (6 proc, 32GB ram) using the classes I join. These classes are based on
my business use case on which I first ran into the issue and the example given
on this page, it is basically joining two flows with massive spilling on disk.
I reproduce the bug starting from 8000 mappers generating each 10 objects
(first two arguments). This is equivalent to about 36GB of spilled data. With
2000 mappers, the bug did not happen. This issue occurs clearly for me when a
massive volume of data is involved. The job takes 1 hour to run on my cluster
with these parameters.
Other paremeters : 18 executors, parallelism level = 36.
was (Author: geynard):
I have been able to reproduce this bug several times on my cluster composed of
4 VMs (6 proc, 32GB ram) using the classes I join. These classes are based on
my business use case on which I first ran into the issue and the example given
on this page, it is basically joining two flows with massive spilling on disk.
I reproduce the bug starting from 8000 mappers generating each 10 objects
(first two arguments). This is equivalent to about 36GB of spilled data. With
2000 mappers, the bug did not happen. This issue occurs clearly for me when a
massive volume of data is involved.
FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based
shuffle
-
Key: SPARK-4105
URL: https://issues.apache.org/jira/browse/SPARK-4105
Project: Spark
Issue Type: Bug
Components: Shuffle, Spark Core
Affects Versions: 1.2.0, 1.2.1, 1.3.0
Reporter: Josh Rosen
Assignee: Josh Rosen
Priority: Blocker
Attachments: JavaObjectToSerialize.java,
SparkFailedToUncompressGenerator.scala
We have seen non-deterministic {{FAILED_TO_UNCOMPRESS(5)}} errors during
shuffle read. Here's a sample stacktrace from an executor:
{code}
14/10/23 18:34:11 ERROR Executor: Exception in task 1747.3 in stage 11.0 (TID
33053)
java.io.IOException: FAILED_TO_UNCOMPRESS(5)
at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:78)
at org.xerial.snappy.SnappyNative.rawUncompress(Native Method)
at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:391)
at org.xerial.snappy.Snappy.uncompress(Snappy.java:427)
at
org.xerial.snappy.SnappyInputStream.readFully(SnappyInputStream.java:127)
at
org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:88)
at org.xerial.snappy.SnappyInputStream.init(SnappyInputStream.java:58)
at
org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:128)
at
org.apache.spark.storage.BlockManager.wrapForCompression(BlockManager.scala:1090)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:116)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:115)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:243)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:52)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at
org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:30)
at
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at
org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:129)
at
org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:159)
at
org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:158)
at
scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at
scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)
at org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:158)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14513761#comment-14513761
]
Guillaume E.B. edited comment on SPARK-4105 at 5/7/15 2:10 PM:
---
I have this issue using spark1.2.0+ shipped with Cloudera CDH5.3. Some more
information:
- This issue showed when I tried to augment the property
spark.default.parallelism in order to have 2 or 3 tasks per executor (in my
case 36 tasks for 18 executors).
- I reproduce it almost every time on my configuration, with Snappy or any
other compression mechanism.
- My cluster is composed of 4 VMs with 6 cores, 32GB RAM on each.
- My use case is performing joins on about 50GB of data compressed with Snappy.
- I use KryoSerialization.
- It generally occurs on just once shuffle partition, but once it has, this
partition will throw the error 4 times and fail the job.
was (Author: geynard):
I have this issue using spark1.2.0+ shipped with Cloudera CDH5.3. Some more
information:
- This issue showed when I tried to augment the property
spark.default.parallelism in order to have 2 or 3 tasks per executor (in my
case 36 tasks for 18 executors).
- I reproduce it almost every time on my configuration, with Snappy or any
another compression mechanism.
- My cluster is composed of 4 VMs with 6 cores, 32GB RAM on each.
- My use case is performing joins on about 50GB of data compressed with Snappy.
- I use KryoSerialization.
- It generally occurs on just once shuffle partition, but once it has, this
partition will throw the error 4 times and fail the job.
FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based
shuffle
-
Key: SPARK-4105
URL: https://issues.apache.org/jira/browse/SPARK-4105
Project: Spark
Issue Type: Bug
Components: Shuffle, Spark Core
Affects Versions: 1.2.0, 1.2.1, 1.3.0
Reporter: Josh Rosen
Assignee: Josh Rosen
Priority: Blocker
Attachments: JavaObjectToSerialize.java,
SparkFailedToUncompressGenerator.scala
We have seen non-deterministic {{FAILED_TO_UNCOMPRESS(5)}} errors during
shuffle read. Here's a sample stacktrace from an executor:
{code}
14/10/23 18:34:11 ERROR Executor: Exception in task 1747.3 in stage 11.0 (TID
33053)
java.io.IOException: FAILED_TO_UNCOMPRESS(5)
at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:78)
at org.xerial.snappy.SnappyNative.rawUncompress(Native Method)
at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:391)
at org.xerial.snappy.Snappy.uncompress(Snappy.java:427)
at
org.xerial.snappy.SnappyInputStream.readFully(SnappyInputStream.java:127)
at
org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:88)
at org.xerial.snappy.SnappyInputStream.init(SnappyInputStream.java:58)
at
org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:128)
at
org.apache.spark.storage.BlockManager.wrapForCompression(BlockManager.scala:1090)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:116)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:115)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:243)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:52)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at
org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:30)
at
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at
org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:129)
at
org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:159)
at
org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:158)
at
scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at
scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)
at org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:158)
at
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14532692#comment-14532692
]
Guillaume E.B. edited comment on SPARK-4105 at 5/7/15 2:16 PM:
---
I have been able to reproduce this bug several times on my cluster composed of
4 VMs (6 proc, 32GB ram) using the classes I join. These classes are based on
my business use case on which I first ran into the issue and the example given
on this page, it is basically joining two flows with massive spilling on disk.
I reproduce the bug starting from 8000 mappers generating each 10 objects
(first two arguments). This is equivalent to about 36GB of spilled data. With
2000 mappers, the bug did not happen. This issue occurs clearly for me when a
massive volume of data is involved. The job takes 1 hour to run on my cluster
with these parameters.
Other parameters: 18 executors, parallelism level = 36.
Spar version: 1.3.0, cdh5.4
was (Author: geynard):
I have been able to reproduce this bug several times on my cluster composed of
4 VMs (6 proc, 32GB ram) using the classes I join. These classes are based on
my business use case on which I first ran into the issue and the example given
on this page, it is basically joining two flows with massive spilling on disk.
I reproduce the bug starting from 8000 mappers generating each 10 objects
(first two arguments). This is equivalent to about 36GB of spilled data. With
2000 mappers, the bug did not happen. This issue occurs clearly for me when a
massive volume of data is involved. The job takes 1 hour to run on my cluster
with these parameters.
Other paremeters : 18 executors, parallelism level = 36.
FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based
shuffle
-
Key: SPARK-4105
URL: https://issues.apache.org/jira/browse/SPARK-4105
Project: Spark
Issue Type: Bug
Components: Shuffle, Spark Core
Affects Versions: 1.2.0, 1.2.1, 1.3.0
Reporter: Josh Rosen
Assignee: Josh Rosen
Priority: Blocker
Attachments: JavaObjectToSerialize.java,
SparkFailedToUncompressGenerator.scala
We have seen non-deterministic {{FAILED_TO_UNCOMPRESS(5)}} errors during
shuffle read. Here's a sample stacktrace from an executor:
{code}
14/10/23 18:34:11 ERROR Executor: Exception in task 1747.3 in stage 11.0 (TID
33053)
java.io.IOException: FAILED_TO_UNCOMPRESS(5)
at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:78)
at org.xerial.snappy.SnappyNative.rawUncompress(Native Method)
at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:391)
at org.xerial.snappy.Snappy.uncompress(Snappy.java:427)
at
org.xerial.snappy.SnappyInputStream.readFully(SnappyInputStream.java:127)
at
org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:88)
at org.xerial.snappy.SnappyInputStream.init(SnappyInputStream.java:58)
at
org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:128)
at
org.apache.spark.storage.BlockManager.wrapForCompression(BlockManager.scala:1090)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:116)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:115)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:243)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:52)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at
org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:30)
at
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at
org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:129)
at
org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:159)
at
org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:158)
at
scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at
scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)
at
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[ https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14532692#comment-14532692 ] Guillaume E.B. edited comment on SPARK-4105 at 5/7/15 3:08 PM: --- I have been able to reproduce this bug several times on my cluster composed of 4 VMs (6 proc, 32GB ram) using the classes I join. These classes are based on my business use case on which I first ran into the issue and the example given on this page, it is basically joining two flows with massive spilling on disk. I reproduce the bug starting from 8000 mappers generating each 10 objects (first two arguments). This is equivalent to about 36GB of spilled data. With 2000 mappers, the bug did not happen. This issue occurs clearly for me when a massive volume of data is involved. The job takes 1 hour to run on my cluster with these parameters. Other parameters: 18 executors, parallelism level = 36. Spar version: 1.3.0, cdh5.4 Stack trace: com.esotericsoftware.kryo.KryoException: java.io.IOException: failed to uncompress the chunk: FAILED_TO_UNCOMPRESS(5) at com.esotericsoftware.kryo.io.Input.fill(Input.java:142) at com.esotericsoftware.kryo.io.Input.require(Input.java:169) at com.esotericsoftware.kryo.io.Input.readAscii_slow(Input.java:580) at com.esotericsoftware.kryo.io.Input.readAscii(Input.java:558) at com.esotericsoftware.kryo.io.Input.readString(Input.java:436) at com.esotericsoftware.kryo.util.DefaultClassResolver.readName(DefaultClassResolver.java:132) at com.esotericsoftware.kryo.util.DefaultClassResolver.readClass(DefaultClassResolver.java:115) at com.esotericsoftware.kryo.Kryo.readClass(Kryo.java:610) at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:721) at com.twitter.chill.Tuple2Serializer.read(TupleSerializers.scala:42) at com.twitter.chill.Tuple2Serializer.read(TupleSerializers.scala:33) at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:729) at org.apache.spark.serializer.KryoDeserializationStream.readObject(KryoSerializer.scala:138) at org.apache.spark.serializer.DeserializationStream$$anon$1.getNext(Serializer.scala:133) at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:71) at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32) at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371) at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32) at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39) at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) at org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:125) at org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:160) at org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:159) at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771) at org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:159) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61) at org.apache.spark.scheduler.Task.run(Task.scala:64) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:203) 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) Caused by: java.io.IOException: failed to uncompress the chunk: FAILED_TO_UNCOMPRESS(5) at org.xerial.snappy.SnappyInputStream.hasNextChunk(SnappyInputStream.java:361) at org.xerial.snappy.SnappyInputStream.rawRead(SnappyInputStream.java:158) at
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14532980#comment-14532980
]
Patrick Wendell edited comment on SPARK-4105 at 5/7/15 4:46 PM:
[~bherta] can you give a bit more information about your environment? E.g. what
Java version, OS versions and whether you are running Spark inside of
containers or VM's? This bug data corruption caused by a race condition
somewhere and we aren't sure if it might be in OS or JVM libraries or in Spark.
was (Author: pwendell):
[~bherta] can you give a bit more information about your environment? E.g. what
Java version, OS versions and whether you are running Spark inside of
containers or VM's? This bug is a race condition somewhere and we aren't sure
if it might be in OS or JVM libraries or in Spark.
FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based
shuffle
-
Key: SPARK-4105
URL: https://issues.apache.org/jira/browse/SPARK-4105
Project: Spark
Issue Type: Bug
Components: Shuffle, Spark Core
Affects Versions: 1.2.0, 1.2.1, 1.3.0
Reporter: Josh Rosen
Assignee: Josh Rosen
Priority: Blocker
Attachments: JavaObjectToSerialize.java,
SparkFailedToUncompressGenerator.scala
We have seen non-deterministic {{FAILED_TO_UNCOMPRESS(5)}} errors during
shuffle read. Here's a sample stacktrace from an executor:
{code}
14/10/23 18:34:11 ERROR Executor: Exception in task 1747.3 in stage 11.0 (TID
33053)
java.io.IOException: FAILED_TO_UNCOMPRESS(5)
at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:78)
at org.xerial.snappy.SnappyNative.rawUncompress(Native Method)
at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:391)
at org.xerial.snappy.Snappy.uncompress(Snappy.java:427)
at
org.xerial.snappy.SnappyInputStream.readFully(SnappyInputStream.java:127)
at
org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:88)
at org.xerial.snappy.SnappyInputStream.init(SnappyInputStream.java:58)
at
org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:128)
at
org.apache.spark.storage.BlockManager.wrapForCompression(BlockManager.scala:1090)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:116)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:115)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:243)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:52)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at
org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:30)
at
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at
org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:129)
at
org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:159)
at
org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:158)
at
scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at
scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)
at org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:158)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at
org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at
org.apache.spark.rdd.FlatMappedValuesRDD.compute(FlatMappedValuesRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14532980#comment-14532980
]
Patrick Wendell edited comment on SPARK-4105 at 5/7/15 4:47 PM:
[~bherta] can you give a bit more information about your environment? E.g. what
Java version, OS versions and whether you are running Spark inside of
containers or VM's? This bug involves data corruption caused by a race
condition somewhere and we aren't sure if it might be in OS or JVM libraries or
in Spark.
was (Author: pwendell):
[~bherta] can you give a bit more information about your environment? E.g. what
Java version, OS versions and whether you are running Spark inside of
containers or VM's? This bug data corruption caused by a race condition
somewhere and we aren't sure if it might be in OS or JVM libraries or in Spark.
FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based
shuffle
-
Key: SPARK-4105
URL: https://issues.apache.org/jira/browse/SPARK-4105
Project: Spark
Issue Type: Bug
Components: Shuffle, Spark Core
Affects Versions: 1.2.0, 1.2.1, 1.3.0
Reporter: Josh Rosen
Assignee: Josh Rosen
Priority: Blocker
Attachments: JavaObjectToSerialize.java,
SparkFailedToUncompressGenerator.scala
We have seen non-deterministic {{FAILED_TO_UNCOMPRESS(5)}} errors during
shuffle read. Here's a sample stacktrace from an executor:
{code}
14/10/23 18:34:11 ERROR Executor: Exception in task 1747.3 in stage 11.0 (TID
33053)
java.io.IOException: FAILED_TO_UNCOMPRESS(5)
at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:78)
at org.xerial.snappy.SnappyNative.rawUncompress(Native Method)
at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:391)
at org.xerial.snappy.Snappy.uncompress(Snappy.java:427)
at
org.xerial.snappy.SnappyInputStream.readFully(SnappyInputStream.java:127)
at
org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:88)
at org.xerial.snappy.SnappyInputStream.init(SnappyInputStream.java:58)
at
org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:128)
at
org.apache.spark.storage.BlockManager.wrapForCompression(BlockManager.scala:1090)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:116)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:115)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:243)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:52)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at
org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:30)
at
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at
org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:129)
at
org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:159)
at
org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:158)
at
scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at
scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)
at org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:158)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at
org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at
org.apache.spark.rdd.FlatMappedValuesRDD.compute(FlatMappedValuesRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14370598#comment-14370598
]
Harry Brundage edited comment on SPARK-4105 at 3/20/15 2:46 AM:
Would just like to add that we are seeing this occasionally as well on 1.3 and
master from github. It seems mostly to manifest when there is high IO
contention. We were at one point using spinning disks for {{spark.local.dir}}
and that seems to exacerbate the issue. Anybody have any leads?
was (Author: airhorns):
Would just like to add that we are seeing this occasionally as well. It seems
mostly to manifest when there is high IO contention. We were at one point using
spinning disks for {{spark.local.dir}} and that seems to exacerbate the issue.
Anybody have any leads?
FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based
shuffle
-
Key: SPARK-4105
URL: https://issues.apache.org/jira/browse/SPARK-4105
Project: Spark
Issue Type: Bug
Components: Shuffle, Spark Core
Affects Versions: 1.2.0, 1.3.0, 1.2.1
Reporter: Josh Rosen
Assignee: Josh Rosen
Priority: Blocker
We have seen non-deterministic {{FAILED_TO_UNCOMPRESS(5)}} errors during
shuffle read. Here's a sample stacktrace from an executor:
{code}
14/10/23 18:34:11 ERROR Executor: Exception in task 1747.3 in stage 11.0 (TID
33053)
java.io.IOException: FAILED_TO_UNCOMPRESS(5)
at org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:78)
at org.xerial.snappy.SnappyNative.rawUncompress(Native Method)
at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:391)
at org.xerial.snappy.Snappy.uncompress(Snappy.java:427)
at
org.xerial.snappy.SnappyInputStream.readFully(SnappyInputStream.java:127)
at
org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:88)
at org.xerial.snappy.SnappyInputStream.init(SnappyInputStream.java:58)
at
org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:128)
at
org.apache.spark.storage.BlockManager.wrapForCompression(BlockManager.scala:1090)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:116)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator$$anon$1$$anonfun$onBlockFetchSuccess$1.apply(ShuffleBlockFetcherIterator.scala:115)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:243)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:52)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at
org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:30)
at
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at
org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:129)
at
org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:159)
at
org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:158)
at
scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at
scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)
at org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:158)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at
org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at
org.apache.spark.rdd.FlatMappedValuesRDD.compute(FlatMappedValuesRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
at
[jira] [Comment Edited] (SPARK-4105) FAILED_TO_UNCOMPRESS(5) errors when fetching shuffle data with sort-based shuffle
[
https://issues.apache.org/jira/browse/SPARK-4105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14249381#comment-14249381
]
Victor Tso edited comment on SPARK-4105 at 12/17/14 3:11 AM:
-
Similarly, I hit this in 1.1.1:
{code}
Job aborted due to stage failure: Task 0 in stage 3919.0 failed 4 times, most
recent failure: Lost task 0.3 in stage 3919.0 (TID 2467, ...):
java.io.IOException: failed to uncompress the chunk: FAILED_TO_UNCOMPRESS(5)
org.xerial.snappy.SnappyInputStream.hasNextChunk(SnappyInputStream.java:362)
org.xerial.snappy.SnappyInputStream.read(SnappyInputStream.java:384)
java.io.ObjectInputStream$PeekInputStream.peek(ObjectInputStream.java:2293)
java.io.ObjectInputStream$BlockDataInputStream.peek(ObjectInputStream.java:2586)
java.io.ObjectInputStream$BlockDataInputStream.peekByte(ObjectInputStream.java:2596)
java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1318)
java.io.ObjectInputStream.readArray(ObjectInputStream.java:1706)
java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1344)
java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1990)
java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1915)
java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798)
java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1990)
java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1915)
java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798)
java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
java.io.ObjectInputStream.readObject(ObjectInputStream.java:370)
org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:62)
org.apache.spark.serializer.DeserializationStream$$anon$1.getNext(Serializer.scala:133)
org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:71)
org.apache.spark.storage.BlockManager$LazyProxyIterator$1.hasNext(BlockManager.scala:1171)
scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:30)
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:144)
org.apache.spark.Aggregator.combineValuesByKey(Aggregator.scala:58)
org.apache.spark.shuffle.hash.HashShuffleReader.read(HashShuffleReader.scala:48)
org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:92)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
{code}
was (Author: silvermast):
Similarly, I hit this in 1.1.1:
Job aborted due to stage failure: Task 0 in stage 3919.0 failed 4 times, most
recent failure: Lost task 0.3 in stage 3919.0 (TID 2467,
preprod-e1-sw22s.paxata.com): java.io.IOException: failed to uncompress the
chunk: FAILED_TO_UNCOMPRESS(5)
org.xerial.snappy.SnappyInputStream.hasNextChunk(SnappyInputStream.java:362)
org.xerial.snappy.SnappyInputStream.read(SnappyInputStream.java:384)
java.io.ObjectInputStream$PeekInputStream.peek(ObjectInputStream.java:2293)
java.io.ObjectInputStream$BlockDataInputStream.peek(ObjectInputStream.java:2586)
java.io.ObjectInputStream$BlockDataInputStream.peekByte(ObjectInputStream.java:2596)
java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1318)
java.io.ObjectInputStream.readArray(ObjectInputStream.java:1706)
java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1344)
java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1990)
java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1915)
java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798)
java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1990)
java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1915)
java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798)
java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
java.io.ObjectInputStream.readObject(ObjectInputStream.java:370)
org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:62)
org.apache.spark.serializer.DeserializationStream$$anon$1.getNext(Serializer.scala:133)
org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:71)
