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yangwnejia commented on SPARK-4105: ----------------------------------- I Have the same problem in Spark 1.4.0, it gives me these error: 1. org.apache.spark.shuffle.FetchFailedException: FAILED_TO_UNCOMPRESS(5) 2. org.apache.spark.shuffle.FetchFailedException: PARSING_ERROR(2) 3. org.apache.spark.shuffle.MetadataFetchFailedException: Missing an output location for shuffle 10 > 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.<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$WithFilter.foreach(TraversableLike.scala:771) > at org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:158) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > at > org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > at > org.apache.spark.rdd.FlatMappedValuesRDD.compute(FlatMappedValuesRDD.scala:31) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) > at org.apache.spark.scheduler.Task.run(Task.scala:56) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:181) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) > at java.lang.Thread.run(Thread.java:745) > {code} > Here's another occurrence of a similar error: > {code} > java.io.IOException: failed to read chunk > > org.xerial.snappy.SnappyInputStream.hasNextChunk(SnappyInputStream.java:348) > > org.xerial.snappy.SnappyInputStream.rawRead(SnappyInputStream.java:159) > org.xerial.snappy.SnappyInputStream.read(SnappyInputStream.java:142) > > java.io.ObjectInputStream$PeekInputStream.read(ObjectInputStream.java:2310) > > java.io.ObjectInputStream$BlockDataInputStream.read(ObjectInputStream.java:2712) > > java.io.ObjectInputStream$BlockDataInputStream.readFully(ObjectInputStream.java:2742) > java.io.ObjectInputStream.readArray(ObjectInputStream.java:1687) > 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.readObject(ObjectInputStream.java:370) > > org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:62) > > org.apache.spark.serializer.DeserializationStream$$anon$1.getNext(Serializer.scala:133) > org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:71) > scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371) > > org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:30) > > org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39) > > org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:129) > org.apache.spark.Aggregator.combineValuesByKey(Aggregator.scala:58) > > org.apache.spark.shuffle.hash.HashShuffleReader.read(HashShuffleReader.scala:46) > org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:92) > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61) > org.apache.spark.scheduler.Task.run(Task.scala:56) > org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:182) > > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) > > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) > java.lang.Thread.run(Thread.java:745) > {code} > The first stacktrace was reported by a Spark user. The second stacktrace > occurred when running > {code} > import java.util.Random > val numKeyValPairs=1000 > val numberOfMappers=200 > val keySize=10000 > for (i <- 0 to 19) { > val pairs1 = sc.parallelize(0 to numberOfMappers, > numberOfMappers).flatMap(p=>{ > val randGen = new Random > val arr1 = new Array[(Int, Array[Byte])](numKeyValPairs) > for (i <- 0 until numKeyValPairs){ > val byteArr = new Array[Byte](keySize) > randGen.nextBytes(byteArr) > arr1(i) = (randGen.nextInt(Int.MaxValue),byteArr) > } > arr1 > }) > pairs1.groupByKey(numberOfMappers).count > } > {code} > This job frequently runs without any problems, but when it fails it seem that > every post-shuffle task fails with either PARSING_ERROR(2), > FAILED_TO_UNCOMPRESS(5), or some other decompression error. I've seen > reports of similar problems when using LZF compression, so I think that this > is caused by some sort of general stream corruption issue. > This issue has been observed even when no spilling occurs, so I don't believe > that this is due to a bug in spilling code. > I was unable to reproduce this when running this code in a fresh Spark EC2 > cluster and we've been having a hard time finding a deterministic > reproduction. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org