Hi Shixiong, Thanks for your answer. I will take a lot to your suggestion, maybe my call to SparkContext.parallelize doesn't work well when there are less records to parallelize than partitions.
Thanks a lot for your help Greetings, Juan 2015-09-24 2:04 GMT-07:00 Shixiong Zhu <zsxw...@gmail.com>: > 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 >> >> >> >> >> >
