我开发了一个用于测试Spark Streaming转换的ScalaCheck属性。为此，我必须开发一个自定义的InputDStream，它与QueueInputDStream非常相似，但有一种方法可以为dstreams添加新的测试用例，这些对象是Seq [Seq [A]]类型的对象，可以添加到DStream中。您可以在https://github.com/juanrh/sscheck/blob/32c2bff66aa5500182e0162a24ecca6d47707c42/src/main/scala/org/apache/spark/streaming/dstream/DynSeqQueueInputDStream.scala查看代码。我开发了一些以本地模式https://github.com/juanrh/sscheck/blob/32c2bff66aa5500182e0162a24ecca6d47707c42/src/test/scala/es/ucm/fdi/sscheck/spark/streaming/ScalaCheckStreamingTest.scala运行的属性。问题是，当批处理间隔太小，并且机器无法足够快地完成批处理时，我在Spark日志中得到以下异常
15/08/26 11:22:02 ERROR JobScheduler: Error generating jobs for time 1440580922500 ms
java.lang.NullPointerException
at org.apache.spark.streaming.dstream.DStream$$anonfun$count$1$$anonfun$apply$18.apply(DStream.scala:587)
at org.apache.spark.streaming.dstream.DStream$$anonfun$count$1$$anonfun$apply$18.apply(DStream.scala:587)
at org.apache.spark.streaming.dstream.DStream$$anonfun$transform$1$$anonfun$apply$21.apply(DStream.scala:654)
at org.apache.spark.streaming.dstream.DStream$$anonfun$transform$1$$anonfun$apply$21.apply(DStream.scala:654)
at org.apache.spark.streaming.dstream.DStream$$anonfun$transform$2$$anonfun$5.apply(DStream.scala:668)
at org.apache.spark.streaming.dstream.DStream$$anonfun$transform$2$$anonfun$5.apply(DStream.scala:666)
at org.apache.spark.streaming.dstream.TransformedDStream.compute(TransformedDStream.scala:41)
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:399)
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.ShuffledDStream.compute(ShuffledDStream.scala:41)
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:399)
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.MappedDStream.compute(MappedDStream.scala:35)
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:399)
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.scala:38)
at org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:120)
at org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:120)
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:120)
at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$2.apply(JobGenerator.scala:243)
at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$2.apply(JobGenerator.scala:241)
at scala.util.Try$.apply(Try.scala:161)
at org.apache.spark.streaming.scheduler.JobGenerator.generateJobs(JobGenerator.scala:241)
at org.apache.spark.streaming.scheduler.JobGenerator.org$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:177)
at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:83)
at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:82)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
15/08/26 11:22:02 ERROR JobScheduler: Error generating jobs for time 1440580922600 ms
有时测试用例在发生这种情况时仍能正确完成，但我有点担心，并想检查我的自定义InputDStream是否正常。在上一个主题http://apache-spark-user-list.1001560.n3.nabble.com/NullPointerException-from-count-foreachRDD-Resolved-td2066.html中，建议的解决方案是在批处理为空时返回compute（）上的一些空RDD。但是这个解决方案对我来说并不起作用，因为当我这样做时，批次就会混淆（有时两个连续的批次在一个批次中融合，留下一个批次的空白），因此测试用例的完整性由ScalaCheck未保留。此外，QueueuInputDStream在没有批处理时返回None。我想了解为什么Option [RDD [T]]是DStream.compute（）的返回类型，如果我的自定义InputDStream没问题，请检查列表
非常感谢你的帮助。
问候，