Spark 作业在 YARN 模式下失败

Question

我有一个用 Scala 编写的 Spark 程序，它从 HDFS 读取一个 CSV 文件，计算一个新列并将其保存为 parquet 文件。我在 YARN 集群中运行程序。但是每次我尝试启动它时，执行程序都会在某个时候失败并出现此错误。

您能帮我找出可能导致此错误的原因吗？

从执行器登录

16/10/27 15:58:10 WARN storage.BlockManager: Putting block rdd_12_225 failed due to an exception
16/10/27 15:58:10 WARN storage.BlockManager: Block rdd_12_225 could not be removed as it was not found on disk or in memory
16/10/27 15:58:10 ERROR executor.Executor: Exception in task 225.0 in stage 4.0 (TID 465)
java.io.IOException: Stream is corrupted
    at org.apache.spark.io.LZ4BlockInputStream.refill(LZ4BlockInputStream.java:211)
    at org.apache.spark.io.LZ4BlockInputStream.read(LZ4BlockInputStream.java:125)
    at java.io.BufferedInputStream.fill(BufferedInputStream.java:246)
    at java.io.BufferedInputStream.read(BufferedInputStream.java:265)
    at java.io.DataInputStream.readInt(DataInputStream.java:387)
    at org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3$$anon$1.readSize(UnsafeRowSerializer.scala:113)
    at org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3$$anon$1.<init>(UnsafeRowSerializer.scala:120)
    at org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3.asKeyValueIterator(UnsafeRowSerializer.scala:110)
    at org.apache.spark.shuffle.BlockStoreShuffleReader$$anonfun$3.apply(BlockStoreShuffleReader.scala:66)
    at org.apache.spark.shuffle.BlockStoreShuffleReader$$anonfun$3.apply(BlockStoreShuffleReader.scala:62)
    at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
    at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
    at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
    at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
    at org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$3$$anon$1.next(InMemoryRelation.scala:118)
    at org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$3$$anon$1.next(InMemoryRelation.scala:110)
    at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:214)
    at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:935)
    at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:926)
    at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:866)
    at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:926)
    at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:670)
    at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:330)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:281)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
    at org.apache.spark.scheduler.Task.run(Task.scala:86)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
Caused by: net.jpountz.lz4.LZ4Exception: Error decoding offset 15385 of input buffer
    at net.jpountz.lz4.LZ4JNIFastDecompressor.decompress(LZ4JNIFastDecompressor.java:39)
    at org.apache.spark.io.LZ4BlockInputStream.refill(LZ4BlockInputStream.java:205)
    ... 41 more

编辑：

有使用的代码

var df = spark.read.option("header", "true").option("inferSchema", "true").option("treatEmptyValuesAsNulls", "true").csv(hdfsFileURLIn).repartition(nPartitions)
df.printSchema()
df = df.withColumn("ipix", a2p(df.col(deName), df.col(raName))).persist(StorageLevel.MEMORY_AND_DISK)
df.repartition(nPartitions, $"ipix").write.mode("overwrite").option("spark.hadoop.dfs.replication", 1).parquet(hdfsFileURLOut)

用户函数 a2p 只取两个 Double 并返回另一个 double

我需要说，这适用于相对较小的 CSV（~1Go），但每次使用较大的 CSV（~15Go）时都会发生此错误

编辑 2： 按照建议我禁用了重新分区并使用了 StorageLevel.DISK_ONLY

有了这个，我没有得到 Putting block rdd_***** failed 由于异常，但仍然存在与 LZ4 相关的异常（流已损坏）：

16/10/28 07:53:00 ERROR util.Utils: Aborting task
java.io.IOException: Stream is corrupted
    at org.apache.spark.io.LZ4BlockInputStream.refill(LZ4BlockInputStream.java:211)
    at org.apache.spark.io.LZ4BlockInputStream.available(LZ4BlockInputStream.java:109)
    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: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:127)
    at org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$3$$anon$1.next(UnsafeRowSerializer.scala:110)
    at scala.collection.Iterator$$anon$12.next(Iterator.scala:444)
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
    at org.apache.spark.util.CompletionIterator.next(CompletionIterator.scala:30)
    at org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
    at org.apache.spark.sql.execution.datasources.DefaultWriterContainer$$anonfun$writeRows$1.apply$mcV$sp(WriterContainer.scala:254)
    at org.apache.spark.sql.execution.datasources.DefaultWriterContainer$$anonfun$writeRows$1.apply(WriterContainer.scala:252)
    at org.apache.spark.sql.execution.datasources.DefaultWriterContainer$$anonfun$writeRows$1.apply(WriterContainer.scala:252)
    at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1345)
    at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:258)
    at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(InsertIntoHadoopFsRelationCommand.scala:143)
    at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(InsertIntoHadoopFsRelationCommand.scala:143)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
    at org.apache.spark.scheduler.Task.run(Task.scala:86)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
Caused by: net.jpountz.lz4.LZ4Exception: Error decoding offset 12966 of input buffer
    at net.jpountz.lz4.LZ4JNIFastDecompressor.decompress(LZ4JNIFastDecompressor.java:39)
    at org.apache.spark.io.LZ4BlockInputStream.refill(LZ4BlockInputStream.java:205)
    ... 25 more

编辑 3：我通过删除第二个重新分区（使用 ipix 列重新分区的那个）成功地启动它而没有任何错误我将在此方法的文档中进一步查看

编辑 4：这很奇怪，有时一些执行程序会因分段错误而失败：

#
# A fatal error has been detected by the Java Runtime Environment:
#
#  SIGSEGV (0xb) at pc=0x00007f48d8a47f2c, pid=3501, tid=0x00007f48cc60c700
#
# JRE version: Java(TM) SE Runtime Environment (8.0_102-b14) (build 1.8.0_102-b14)
# Java VM: Java HotSpot(TM) 64-Bit Server VM (25.102-b14 mixed mode linux-amd64 compressed oops)
# Problematic frame:
# J 4713 C2 org.apache.spark.unsafe.types.UTF8String.hashCode()I (18 bytes) @ 0x00007f48d8a47f2c [0x00007f48d8a47e60+0xcc]
#
# Core dump written. Default location: /tmp/hadoop-root/nm-local-dir/usercache/root/appcache/application_1477580152295_0008/container_1477580152295_0008_01_000006/core or core.3501
#
# An error report file with more information is saved as:
# /tmp/hadoop-root/nm-local-dir/usercache/root/appcache/application_1477580152295_0008/container_1477580152295_0008_01_000006/hs_err_pid3501.log
#
# If you would like to submit a bug report, please visit:
#   http://bugreport.java.com/bugreport/crash.jsp
#

我检查了内存，我所有的执行程序总是有足够的空闲内存（至少 6Go）

编辑 4：所以我用多个文件进行了测试，并且执行总是成功，但有时一些执行程序失败（出现上述错误）并由 YARN 再次启动

Answer 1

您使用的是哪个版本的 lz4-java？这可能与版本 1.1.2 中修复的问题有关——请参阅此bug report

另外，我对你的功能 a2p 很好奇。理想情况下，它应该将两个 Column 对象作为输入，而不仅仅是 Doubles（除非您将其注册为 UDF）。

Answer 2

遇到了同样的问题。

症状与problem: SPARK-18105 完全一样。

截至 2017 年 1 月 29 日，尚未修复。

Answer 3

我在 SPARK_HOME 路径的 jars 目录中将 lz4-java jar 替换为最新版本 (lz4-java-1.5.0.jar)。这对我有用。