Spark抛出java.util.NoSuchElementException:找不到键:67 [英] Spark throws java.util.NoSuchElementException: key not found: 67

问题描述

在齐柏林飞艇中运行Spark二等分kmmeans算法.

Running the Spark bisecting kmmeans algorithm in Zeppelin.

//I transform my data using the TF-IDF algorithm 

val idf = new IDF(minFreq).fit(data)
val hashIDF_features = idf.transform(dbTF)    

//and parse the transformed data to the clustering algorithm.

val bkm = new BisectingKMeans().setK(100).setMaxIterations(2)
val model = bkm.run(hashIDF_features)
val cluster_rdd = model.predict(hashIDF_features)

我总是会收到此错误:

org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 270.0 failed 4 times, most recent failure: Lost task 0.3 in stage 270.0 (TID 126885, IP): java.util.NoSuchElementException: key not found: 67
    at scala.collection.MapLike$class.default(MapLike.scala:228)
    at scala.collection.AbstractMap.default(Map.scala:58)
    at scala.collection.MapLike$class.apply(MapLike.scala:141)
    at scala.collection.AbstractMap.apply(Map.scala:58)
    at org.apache.spark.mllib.clustering.BisectingKMeans$$anonfun$org$apache$spark$mllib$clustering$BisectingKMeans$$updateAssignments$1$$anonfun$2.apply$mcDJ$sp(BisectingKMeans.scala:338)
    at org.apache.spark.mllib.clustering.BisectingKMeans$$anonfun$org$apache$spark$mllib$clustering$BisectingKMeans$$updateAssignments$1$$anonfun$2.apply(BisectingKMeans.scala:337)
    at org.apache.spark.mllib.clustering.BisectingKMeans$$anonfun$org$apache$spark$mllib$clustering$BisectingKMeans$$updateAssignments$1$$anonfun$2.apply(BisectingKMeans.scala:337)
    at scala.collection.TraversableOnce$$anonfun$minBy$1.apply(TraversableOnce.scala:231)
    at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)
    at scala.collection.immutable.List.foldLeft(List.scala:84)
    at scala.collection.LinearSeqOptimized$class.reduceLeft(LinearSeqOptimized.scala:125)
    at scala.collection.immutable.List.reduceLeft(List.scala:84)
    at scala.collection.TraversableOnce$class.minBy(TraversableOnce.scala:231)
    at scala.collection.AbstractTraversable.minBy(Traversable.scala:105)
    at org.apache.spark.mllib.clustering.BisectingKMeans$$anonfun$org$apache$spark$mllib$clustering$BisectingKMeans$$updateAssignments$1.apply(BisectingKMeans.scala:337)
    at org.apache.spark.mllib.clustering.BisectingKMeans$$anonfun$org$apache$spark$mllib$clustering$BisectingKMeans$$updateAssignments$1.apply(BisectingKMeans.scala:334)
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
    at scala.collection.Iterator$$anon$14.hasNext(Iterator.scala:389)
    at org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:189)
    at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:64)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
    at org.apache.spark.scheduler.Task.run(Task.scala:89)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1433)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1421)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1420)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1420)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:801)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:801)
    at scala.Option.foreach(Option.scala:236)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:801)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1642)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1601)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1590)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:622)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1856)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1869)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1882)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1953)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:934)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:323)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:933)
    at org.apache.spark.mllib.clustering.BisectingKMeans$.org$apache$spark$mllib$clustering$BisectingKMeans$$summarize(BisectingKMeans.scala:261)
    at org.apache.spark.mllib.clustering.BisectingKMeans$$anonfun$run$1.apply$mcVI$sp(BisectingKMeans.scala:194)
    at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:141)
    at org.apache.spark.mllib.clustering.BisectingKMeans.run(BisectingKMeans.scala:189)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$$$93297bcd59dca476dd569cf51abed168$$$$$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:89)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$$$93297bcd59dca476dd569cf51abed168$$$$$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:95)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$$$93297bcd59dca476dd569cf51abed168$$$$$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:97)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$$$93297bcd59dca476dd569cf51abed168$$$$$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:99)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$$$93297bcd59dca476dd569cf51abed168$$$$$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:101)

我正在使用Spark 1.6.1. 有趣的是，如果我在一个独立的应用程序上运行该算法，它不会出现任何错误，但是我在Zeppelin中得到了它.除此之外，输入是通过外部算法计算出来的，因此我认为这不是格式问题.有什么想法吗?

Im using Spark 1.6.1. Interestingly, if I run this algorithm on a standalone application, it hits no errors, but I get this in Zeppelin. In addition to that, the input has been calculated by an external algorithm so I dont think it is a formatting problem. Any ideas?

修改:
我通过使用较少量的群集再次测试了系统，并且不会发生该错误.为什么对于较大的聚类值，算法会失败?

I tested the system again by using smaller amounts of clusters, and the error does not happen. Why would the algorithm break for large cluster values?

推荐答案

我认为该问题是由于这将有助于解决您的问题.

I believe the issue is because of closure. When you run your application locally, everything might be running in same memory/process.So make sure you are not trying to access local variable from a clousre which might be running in some other memory/process. This will be helpful to resolve your issue.

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