org.apache.spark.SparkException: Job aborted: Task 0.0:1 failed 32 times (most recent failure: Exception failure: java.lang.IllegalStateException: unread block data)

Apache's JIRA Issue Tracker | sam | 3 years ago
  1. 0

    Apache Spark Throws java.lang.IllegalStateException: unread block data

    Stack Overflow | 3 years ago | samthebest
    org.apache.spark.SparkException: Job aborted: Task 0.0:1 failed 32 times (most recent failure: Exception failure: java.lang.IllegalStateException: unread block data)
  2. 0

    [SPARK-1867] Spark Documentation Error causes java.lang.IllegalStateException: unread block data - ASF JIRA

    apache.org | 11 months ago
    org.apache.spark.SparkException: Job aborted: Task 0.0:1 failed 32 times (most recent failure: Exception failure: java.lang.IllegalStateException: unread block data)
  3. 0

    scala - Apache Spark Throws java.lang.IllegalStateException: unread block data - Stack Overflow

    xluat.com | 1 year ago
    org.apache.spark.SparkException: Job aborted: Task 0.0:1 failed 32 times (most recent failure: Exception failure: java.lang.IllegalStateException: unread block data)
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  5. 0

    Apache Spark Throws java.lang.IllegalStateException: unread block data

    sohu.io | 1 year ago
    org.apache.spark.SparkException: Job aborted: Task 0.0:1 failed 32 times (most recent failure: Exception failure: java.lang.IllegalStateException: unread block data)
  6. 0

    I've employed two System Administrators on a contract basis (for quite a bit of money), and both contractors have independently hit the following exception. What we are doing is: 1. Installing Spark 0.9.1 according to the documentation on the website, along with CDH4 (and another cluster with CDH5) distros of hadoop/hdfs. 2. Building a fat jar with a Spark app with sbt then trying to run it on the cluster I've also included code snippets, and sbt deps at the bottom. When I've Googled this, there seems to be two somewhat vague responses: a) Mismatching spark versions on nodes/user code b) Need to add more jars to the SparkConf Now I know that (b) is not the problem having successfully run the same code on other clusters while only including one jar (it's a fat jar). But I have no idea how to check for (a) - it appears Spark doesn't have any version checks or anything - it would be nice if it checked versions and threw a "mismatching version exception: you have user code using version X and node Y has version Z". I would be very grateful for advice on this. The exception: Exception in thread "main" org.apache.spark.SparkException: Job aborted: Task 0.0:1 failed 32 times (most recent failure: Exception failure: java.lang.IllegalStateException: unread block data) at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1020) at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1018) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$abortStage(DAGScheduler.scala:1018) at org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:604) at org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:604) at scala.Option.foreach(Option.scala:236) at org.apache.spark.scheduler.DAGScheduler.processEvent(DAGScheduler.scala:604) at org.apache.spark.scheduler.DAGScheduler$$anonfun$start$1$$anon$2$$anonfun$receive$1.applyOrElse(DAGScheduler.scala:190) at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498) at akka.actor.ActorCell.invoke(ActorCell.scala:456) at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237) at akka.dispatch.Mailbox.run(Mailbox.scala:219) at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386) at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260) at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339) at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979) at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107) 14/05/16 18:05:31 INFO scheduler.TaskSetManager: Loss was due to java.lang.IllegalStateException: unread block data [duplicate 59] My code snippet: val conf = new SparkConf() .setMaster(clusterMaster) .setAppName(appName) .setSparkHome(sparkHome) .setJars(SparkContext.jarOfClass(this.getClass)) println("count = " + new SparkContext(conf).textFile(someHdfsPath).count()) My SBT dependencies: // relevant "org.apache.spark" % "spark-core_2.10" % "0.9.1", "org.apache.hadoop" % "hadoop-client" % "2.3.0-mr1-cdh5.0.0", // standard, probably unrelated "com.github.seratch" %% "awscala" % "[0.2,)", "org.scalacheck" %% "scalacheck" % "1.10.1" % "test", "org.specs2" %% "specs2" % "1.14" % "test", "org.scala-lang" % "scala-reflect" % "2.10.3", "org.scalaz" %% "scalaz-core" % "7.0.5", "net.minidev" % "json-smart" % "1.2"

    Apache's JIRA Issue Tracker | 3 years ago | sam
    org.apache.spark.SparkException: Job aborted: Task 0.0:1 failed 32 times (most recent failure: Exception failure: java.lang.IllegalStateException: unread block data)

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    Root Cause Analysis

    1. org.apache.spark.SparkException

      Job aborted: Task 0.0:1 failed 32 times (most recent failure: Exception failure: java.lang.IllegalStateException: unread block data)

      at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply()
    2. Spark
      DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply
      1. org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1020)
      2. org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1018)
      2 frames
    3. Scala
      ArrayBuffer.foreach
      1. scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
      2. scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
      2 frames
    4. Spark
      DAGScheduler$$anonfun$processEvent$10.apply
      1. org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$abortStage(DAGScheduler.scala:1018)
      2. org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:604)
      3. org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:604)
      3 frames
    5. Scala
      Option.foreach
      1. scala.Option.foreach(Option.scala:236)
      1 frame
    6. Spark
      DAGScheduler$$anonfun$start$1$$anon$2$$anonfun$receive$1.applyOrElse
      1. org.apache.spark.scheduler.DAGScheduler.processEvent(DAGScheduler.scala:604)
      2. org.apache.spark.scheduler.DAGScheduler$$anonfun$start$1$$anon$2$$anonfun$receive$1.applyOrElse(DAGScheduler.scala:190)
      2 frames
    7. Akka Actor
      ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec
      1. akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
      2. akka.actor.ActorCell.invoke(ActorCell.scala:456)
      3. akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
      4. akka.dispatch.Mailbox.run(Mailbox.scala:219)
      5. akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
      5 frames
    8. Scala
      ForkJoinWorkerThread.run
      1. scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
      2. scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
      3. scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
      4. scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
      4 frames