apache-spark - 在Amazon EMR集群中运行时，Spark广播的变量返回NullPointerException

Question

我通过广播共享的变量在集群中为空。

我的应用程序很复杂，但是我写了这个小例子，当我在本地运行它时，它可以完美运行，但是在集群中失败了:

package com.gonzalopezzi.bigdata.bicing

import org.apache.spark.broadcast.Broadcast
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkContext, SparkConf}

object PruebaBroadcast2 extends App {
  val conf = new SparkConf().setAppName("PruebaBroadcast2")
  val sc = new SparkContext(conf)

  val arr : Array[Int] = (6 to 9).toArray
  val broadcasted = sc.broadcast(arr)

  val rdd : RDD[Int] = sc.parallelize((1 to 4).toSeq, 2) // a small integer array [1, 2, 3, 4] is paralellized in two machines
  rdd.flatMap((a : Int) => List((a, broadcasted.value(0)))).reduceByKey(_+_).collect().foreach(println)  // NullPointerException in the flatmap. broadcasted is null

}

我不知道问题是编码错误还是配置问题。

这是我得到的堆栈跟踪:

15/07/07 20:55:13 INFO scheduler.DAGScheduler: Job 0 failed: collect at PruebaBroadcast2.scala:24, took 0.992297 s
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0 (TID 6, ip-172-31-36-49.ec2.internal): java.lang.NullPointerException
    at com.gonzalopezzi.bigdata.bicing.PruebaBroadcast2$$anonfun$2.apply(PruebaBroadcast2.scala:24)
    at com.gonzalopezzi.bigdata.bicing.PruebaBroadcast2$$anonfun$2.apply(PruebaBroadcast2.scala:24)
    at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
    at org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:202)
    at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:56)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
    at org.apache.spark.scheduler.Task.run(Task.scala:64)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:203)
    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:745)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1204)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1193)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1192)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1192)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:693)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:693)
    at scala.Option.foreach(Option.scala:236)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:693)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1393)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1354)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
Command exiting with ret '1'

谁能帮我解决这个问题？
至少，您能告诉我代码中是否有奇怪的地方吗？
如果您认为代码还可以，请告诉我，因为这意味着问题出在群集的配置上。

提前致谢。

Answer 1

终于，我开始工作了。

这样声明对象是行不通的:

object MyObject extends App {

但是，如果您使用主函数声明一个对象，它就会起作用:

object MyObject {
    def main (args : Array[String]) {
    /* ... */
    }
}

因此，如果我以这种方式重写它，那么问题中的简短示例将起作用:

object PruebaBroadcast2 {

  def main (args: Array[String]) {
    val conf = new SparkConf().setAppName("PruebaBroadcast2")
    val sc = new SparkContext(conf)

    val arr : Array[Int] = (6 to 9).toArray
    val broadcasted = sc.broadcast(arr)

    val rdd : RDD[Int] = sc.parallelize((1 to 4).toSeq, 2)

    rdd.flatMap((a : Int) => List((a, broadcasted.value(0)))).reduceByKey(_+_).collect().foreach(println)
  }
}

此问题似乎与此错误有关:
https://issues.apache.org/jira/browse/SPARK-4170