[SPARK-11932] trackStateByKey throws java.lang.IllegalArgumentException: requirement failed on restarting from checkpoint

• Type: Bug
• Status: Resolved
• Priority: Critical
• Resolution: Fixed
• Fix Version/s: 1.6.0
• Component/s: DStreams
• Target Version/s: 1.6.0
• Epic Link: Improved State Management
• Assignee: Tathagata Das
• Reporter: Tathagata Das
• Created: 23/Nov/15 21:51
• Updated: 07/Dec/15 19:06
• Resolved: 07/Dec/15 19:06

Description

The problem is that when recovering a streaming application using trackStateByKey from Dstream checkpoints, there is the following exception.

Code

  StreamingContext.getOrCreate(".", () => createContext(args))
  ...

  def createContext(args: Array[String]) : StreamingContext = {

    val sparkConf = new SparkConf().setAppName("StatefulNetworkWordCount")
    // Create the context with a 1 second batch size
    val ssc = new StreamingContext(sparkConf, Seconds(1))
    
    ssc.checkpoint(".")

    // Initial RDD input to trackStateByKey
    val initialRDD = ssc.sparkContext.parallelize(List(("hello", 1), ("world", 1)))

    // Create a ReceiverInputDStream on target ip:port and count the
    // words in input stream of \n delimited test (eg. generated by 'nc')
    val lines = ssc.socketTextStream(args(0), args(1).toInt)
    val words = lines.flatMap(_.split(" "))
    val wordDstream = words.map(x => (x, 1))

    // Update the cumulative count using updateStateByKey
    // This will give a DStream made of state (which is the cumulative count of the words)
    val trackStateFunc = (batchTime: Time, word: String, one: Option[Int], state: State[Int]) => {
      val sum = one.getOrElse(0) + state.getOption.getOrElse(0)
      val output = (word, sum)
      state.update(sum)
      Some(output)
    }

    val stateDstream = wordDstream.trackStateByKey(
      StateSpec.function(trackStateFunc).initialState(initialRDD))
    stateDstream.print()
    
    ssc
  
  }

Error

15/11/23 10:55:07 ERROR StreamingContext: Error starting the context, marking it as stopped
java.lang.IllegalArgumentException: requirement failed
at scala.Predef$.require(Predef.scala:221)
at org.apache.spark.streaming.rdd.TrackStateRDD.<init>(TrackStateRDD.scala:133)
at org.apache.spark.streaming.dstream.InternalTrackStateDStream$$anonfun$compute$2.apply(TrackStateDStream.scala:148)
at org.apache.spark.streaming.dstream.InternalTrackStateDStream$$anonfun$compute$2.apply(TrackStateDStream.scala:143)
at scala.Option.map(Option.scala:145)
at org.apache.spark.streaming.dstream.InternalTrackStateDStream.compute(TrackStateDStream.scala:143)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:350)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:350)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:349)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:349)
at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:424)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:344)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:342)
at scala.Option.orElse(Option.scala:257)
at org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:339)
at org.apache.spark.streaming.dstream.TrackStateDStreamImpl.compute(TrackStateDStream.scala:66)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:350)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:350)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:349
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:349)
at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:424)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:344)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:342)
at scala.Option.orElse(Option.scala:257)
at org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:339)
at org.apache.spark.streaming.dstream.ForEachDStream.generateJob(ForEachDStream.scla:47)
at org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:115)
at org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:114)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:251)
at scala.collection.AbstractTraversable.flatMap(Traversable.scala:105)
at org.apache.spark.streaming.DStreamGraph.generateJobs(DStreamGraph.scala:114)
at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$restart$4.apply(JobGenerator.scala:231)
at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$restart$4.apply(JobGenerator.scala:226)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
at org.apache.spark.streaming.scheduler.JobGenerator.restart(JobGenerator.scala:226
at org.apache.spark.streaming.scheduler.JobGenerator.start(JobGenerator.scala:96)
at org.apache.spark.streaming.scheduler.JobScheduler.start(JobScheduler.scala:83)
at org.apache.spark.streaming.StreamingContext$$anonfun$liftedTree1$1$1.apply$mcV$sp(StreamingContext.scala:609)
at org.apache.spark.streaming.StreamingContext$$anonfun$liftedTree1$1$1.apply(StreamingContext.scala:605)
at org.apache.spark.streaming.StreamingContext$$anonfun$liftedTree1$1$1.apply(StreamingContext.scala:605)
at ... run in separate thread using org.apache.spark.util.ThreadUtils ... ()
at org.apache.spark.streaming.StreamingContext.liftedTree1$1(StreamingContext.scala:605)
at org.apache.spark.streaming.StreamingContext.start(StreamingContext.scala:599)
at org.apache.spark.examples.streaming.StatefulNetworkWordCount$.main(StatefulNetworkWordCount.scala:48)
at org.apache.spark.examples.streaming.StatefulNetworkWordCount.main(StatefulNetworkWordCount.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:483)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:727)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

The reason is that TrackStateRDDs generated by trackStateByKey expect the previous batch's TrackStateRDDs to have a partitioner. However, when recovery from DStream checkpoints, the RDDs recovered from RDD checkpoints do not have a partitioner attached to it. This is because RDD checkpoints do not preserve the partitioner (SPARK-12004).

Issue Links

• depends upon: SPARK-12004 RDD checkpointing does not preserve partitioner
• links to: [Github] Pull Request #9988 (tdas)
• links to: [Github] Pull Request #9989 (tmnd1991)