Looks like you returns a "Some(null)" in "compute". If you don't want to create a RDD, it should return None. If you want to return an empty RDD, it should return "Some(sc.emptyRDD)".
Best Regards, Shixiong Zhu 2015-09-15 2:51 GMT+08:00 Juan Rodríguez Hortalá < juan.rodriguez.hort...@gmail.com>: > Hi, > > I sent this message to the user list a few weeks ago with no luck, so I'm > forwarding it to the dev list in case someone could give a hand with this. > Thanks a lot in advance > > > I've developed a ScalaCheck property for testing Spark Streaming > transformations. To do that I had to develop a custom InputDStream, which > is very similar to QueueInputDStream but has a method for adding new test > cases for dstreams, which are objects of type Seq[Seq[A]], to the DStream. > You can see the code at > https://github.com/juanrh/sscheck/blob/32c2bff66aa5500182e0162a24ecca6d47707c42/src/main/scala/org/apache/spark/streaming/dstream/DynSeqQueueInputDStream.scala. > I have developed a few properties that run in local mode > https://github.com/juanrh/sscheck/blob/32c2bff66aa5500182e0162a24ecca6d47707c42/src/test/scala/es/ucm/fdi/sscheck/spark/streaming/ScalaCheckStreamingTest.scala. > The problem is that when the batch interval is too small, and the machine > cannot complete the batches fast enough, I get the following exceptions in > the Spark log > > 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 > > Sometimes test cases finish correctly anyway when this happens, but I'm a > bit concerned and wanted to check that my custom InputDStream is ok. In a > previous topic > http://apache-spark-user-list.1001560.n3.nabble.com/NullPointerException-from-count-foreachRDD-Resolved-td2066.html > the suggested solution was to return Some of an empty RDD on compute() when > the batch is empty. But that solution doesn't work for me because when I do > that then batches are mixed up (sometimes two consecutive batches are > fused in a single batch, leaving empty one of the batches), so the > integrity of the test case generated by ScalaCheck is not preserved. > Besides, QueueuInputDStream returns None when there is no batch. I would > like to understand why Option[RDD[T]] is the returning type of > DStream.compute(), and check with the list if my custom InputDStream is ok > > Thanks a lot for your help. > > Greetings, > > Juan > > > > >
