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From Juan Rodríguez Hortalá <juan.rodriguez.hort...@gmail.com>
Subject JobScheduler: Error generating jobs for time for custom InputDStream
Date Wed, 26 Aug 2015 11:30:35 GMT
Hi,

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

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