Spark在处理大型shuffle任务时出现java.io.IOException: Filesystem closed

我经常发现spark在处理大型作业时失败，并伴有一个相当无益的无意义异常。工作日志看起来很正常，没有错误，但是它们的状态是"KILLED"。这对于大型洗牌非常常见，所以像.distinct .

这样的操作

问题是，我如何诊断出哪里出了问题，理想情况下，我如何修复它?

考虑到很多这些操作都是单轴的，我一直在通过将数据分成10个块来解决问题，在每个块上运行应用程序，然后在所有结果输出上运行应用程序。换句话说- meta-map-reduce。

14/06/04 12:56:09 ERROR client.AppClient$ClientActor: Master removed our application: FAILED; stopping client
14/06/04 12:56:09 WARN cluster.SparkDeploySchedulerBackend: Disconnected from Spark cluster! Waiting for reconnection...
14/06/04 12:56:09 WARN scheduler.TaskSetManager: Loss was due to java.io.IOException
java.io.IOException: Filesystem closed
    at org.apache.hadoop.hdfs.DFSClient.checkOpen(DFSClient.java:703)
    at org.apache.hadoop.hdfs.DFSInputStream.readWithStrategy(DFSInputStream.java:779)
    at org.apache.hadoop.hdfs.DFSInputStream.read(DFSInputStream.java:840)
    at java.io.DataInputStream.read(DataInputStream.java:149)
    at org.apache.hadoop.io.compress.DecompressorStream.getCompressedData(DecompressorStream.java:159)
    at org.apache.hadoop.io.compress.DecompressorStream.decompress(DecompressorStream.java:143)
    at org.apache.hadoop.io.compress.DecompressorStream.read(DecompressorStream.java:85)
    at java.io.InputStream.read(InputStream.java:101)
    at org.apache.hadoop.util.LineReader.fillBuffer(LineReader.java:180)
    at org.apache.hadoop.util.LineReader.readDefaultLine(LineReader.java:216)
    at org.apache.hadoop.util.LineReader.readLine(LineReader.java:174)
    at org.apache.hadoop.mapred.LineRecordReader.next(LineRecordReader.java:209)
    at org.apache.hadoop.mapred.LineRecordReader.next(LineRecordReader.java:47)
    at org.apache.spark.rdd.HadoopRDD$$anon$1.getNext(HadoopRDD.scala:164)
    at org.apache.spark.rdd.HadoopRDD$$anon$1.getNext(HadoopRDD.scala:149)
    at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:71)
    at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:27)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at scala.collection.Iterator$class.foreach(Iterator.scala:727)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
    at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
    at scala.collection.mutable.ListBuffer.$plus$plus$eq(ListBuffer.scala:176)
    at scala.collection.mutable.ListBuffer.$plus$plus$eq(ListBuffer.scala:45)
    at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
    at scala.collection.AbstractIterator.to(Iterator.scala:1157)
    at scala.collection.TraversableOnce$class.toList(TraversableOnce.scala:257)
    at scala.collection.AbstractIterator.toList(Iterator.scala:1157)
    at $line5.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(<console>:13)
    at $line5.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(<console>:13)
    at org.apache.spark.rdd.RDD$$anonfun$1.apply(RDD.scala:450)
    at org.apache.spark.rdd.RDD$$anonfun$1.apply(RDD.scala:450)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:34)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:241)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:232)
    at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:241)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:232)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:34)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:241)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:232)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:161)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:102)
    at org.apache.spark.scheduler.Task.run(Task.scala:53)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$run$1.apply$mcV$sp(Executor.scala:213)
    at org.apache.spark.deploy.SparkHadoopUtil$$anon$1.run(SparkHadoopUtil.scala:42)
    at org.apache.spark.deploy.SparkHadoopUtil$$anon$1.run(SparkHadoopUtil.scala:41)
    at java.security.AccessController.doPrivileged(Native Method)
    at javax.security.auth.Subject.doAs(Subject.java:415)
    at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1548)
    at org.apache.spark.deploy.SparkHadoopUtil.runAsUser(SparkHadoopUtil.scala:41)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:178)
    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:744)

截至2014年9月1日，这是Spark的"开放改进"。请参阅https://issues.apache.org/jira/browse/SPARK-3052。正如syzza在给定链接中指出的那样，当执行器失败导致此消息时，关闭钩子可能以不正确的顺序完成。我知道你需要更多的调查来找出问题的主要原因(即为什么你的执行人失败了)。如果它是一个大的shuffle，它可能是一个内存不足的错误，导致执行器失败，然后导致Hadoop文件系统在关闭钩子中被关闭。因此，运行该执行器的任务中的RecordReaders会抛出"java.io。IOException:文件系统关闭"异常。我猜它会在以后的版本中修复，然后你会得到更多有用的错误信息:)

呼叫DFSClient.close()或DFSClient.abort()，关闭客户端。下一个文件操作将导致上述异常。

我会试着弄清楚什么叫close()/abort()。您可以在调试器中使用断点，或者修改Hadoop源代码以在这些方法中抛出异常，这样您就可以获得堆栈跟踪。

当spark作业在集群上运行时，可以解决file system closed异常。你可以将spark.executor.cores、spark.driver.cores和spark.akka.threads等属性设置为资源可用性的最大值。当我的数据集非常大，有大约2000万条JSON数据时，我也遇到了同样的问题。我用上述属性修复了它，它像一个魅力一样运行。在我的例子中，我分别将这些属性设置为25、25和20。希望能有所帮助!!

参考链接:

http://spark.apache.org/docs/latest/configuration.html

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