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Posted to issues@spark.apache.org by "Sean R. Owen (Jira)" <ji...@apache.org> on 2019/11/30 16:08:00 UTC
[jira] [Resolved] (SPARK-29998) A corrupted hard disk causes the
task to execute repeatedly on a machine until the job fails
[ https://issues.apache.org/jira/browse/SPARK-29998?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean R. Owen resolved SPARK-29998.
----------------------------------
Resolution: Not A Problem
> A corrupted hard disk causes the task to execute repeatedly on a machine until the job fails
> --------------------------------------------------------------------------------------------
>
> Key: SPARK-29998
> URL: https://issues.apache.org/jira/browse/SPARK-29998
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.4.0
> Reporter: angerszhu
> Priority: Major
>
> Recently, I meet one situation:
> One NodeManager's disk is broken. when task begin to run, it will get jobConf by broadcast, executor's BlockManager failed to create file. and throw IOException.
> {code}
> 19/11/22 15:14:36 INFO org.apache.spark.scheduler.DAGScheduler: "ShuffleMapStage 342 (run at AccessController.java:0) failed in 0.400 s due to Job aborted due to stage failure: Task 21 in st
> age 343.0 failed 4 times, most recent failure: Lost task 21.3 in stage 343.0 (TID 34968, hostname, executor 104): java.io.IOException: Failed to create local dir in /disk
> 11/yarn/local/usercache/username/appcache/application_1573542949548_2889852/blockmgr-a70777d8-5159-48e7-a47e-848df01a831e/3b.
> at org.apache.spark.storage.DiskBlockManager.getFile(DiskBlockManager.scala:70)
> at org.apache.spark.storage.DiskStore.contains(DiskStore.scala:129)
> at org.apache.spark.storage.BlockManager.getLocalValues(BlockManager.scala:605)
> at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$readBroadcastBlock$1$$anonfun$apply$2.apply(TorrentBroadcast.scala:214)
> at scala.Option.getOrElse(Option.scala:121)
> at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$readBroadcastBlock$1.apply(TorrentBroadcast.scala:211)
> at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1326)
> at org.apache.spark.broadcast.TorrentBroadcast.readBroadcastBlock(TorrentBroadcast.scala:207)
> at org.apache.spark.broadcast.TorrentBroadcast._value$lzycompute(TorrentBroadcast.scala:66)
> at org.apache.spark.broadcast.TorrentBroadcast._value(TorrentBroadcast.scala:66)
> at org.apache.spark.broadcast.TorrentBroadcast.getValue(TorrentBroadcast.scala:96)
> at org.apache.spark.broadcast.Broadcast.value(Broadcast.scala:70)
> at org.apache.spark.rdd.HadoopRDD.getJobConf(HadoopRDD.scala:144)
> at org.apache.spark.rdd.HadoopRDD$$anon$1.<init>(HadoopRDD.scala:228)
> at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:224)
> at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:95)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.rdd.UnionRDD.compute(UnionRDD.scala:105)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
> at org.apache.spark.scheduler.Task.run(Task.scala:121)
> at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748)
> {code}
> Since in TaskSetManager.handleFailedTask()
> For this kind of fail reason, it will retry on this Executor until `failedTime > maxTaskFailTime `
> Then this stage failed, total job failed.
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