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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2015/09/23 09:13:04 UTC

[jira] [Assigned] (SPARK-10772) NullPointerException when transform function in DStream returns NULL

     [ https://issues.apache.org/jira/browse/SPARK-10772?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-10772:
------------------------------------

    Assignee:     (was: Apache Spark)

> NullPointerException when transform function in DStream returns NULL 
> ---------------------------------------------------------------------
>
>                 Key: SPARK-10772
>                 URL: https://issues.apache.org/jira/browse/SPARK-10772
>             Project: Spark
>          Issue Type: Bug
>          Components: Streaming
>    Affects Versions: 1.4.1, 1.5.0
>            Reporter: Jack Hu
>            Priority: Minor
>
> NullPointerException raises when transform function returns NULL:
> {quote}
> java.lang.NullPointerException
> 	at org.apache.spark.streaming.dstream.DStream$$anonfun$clearMetadata$3.apply(DStream.scala:442)
> 	at org.apache.spark.streaming.dstream.DStream$$anonfun$clearMetadata$3.apply(DStream.scala:441)
> 	at scala.collection.mutable.HashMap$$anon$2$$anonfun$foreach$3.apply(HashMap.scala:107)
> 	at scala.collection.mutable.HashMap$$anon$2$$anonfun$foreach$3.apply(HashMap.scala:107)
> 	at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:226)
> 	at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:39)
> 	at scala.collection.mutable.HashMap$$anon$2.foreach(HashMap.scala:107)
> 	at org.apache.spark.streaming.dstream.DStream.clearMetadata(DStream.scala:441)
> 	at org.apache.spark.streaming.dstream.DStream$$anonfun$clearMetadata$5.apply(DStream.scala:454)
> 	at org.apache.spark.streaming.dstream.DStream$$anonfun$clearMetadata$5.apply(DStream.scala:454)
> 	at scala.collection.immutable.List.foreach(List.scala:318)
> 	at org.apache.spark.streaming.dstream.DStream.clearMetadata(DStream.scala:454)
> 	at org.apache.spark.streaming.DStreamGraph$$anonfun$clearMetadata$2.apply(DStreamGraph.scala:129)
> 	at org.apache.spark.streaming.DStreamGraph$$anonfun$clearMetadata$2.apply(DStreamGraph.scala:129)
> 	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> 	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> 	at org.apache.spark.streaming.DStreamGraph.clearMetadata(DStreamGraph.scala:129)
> 	at org.apache.spark.streaming.scheduler.JobGenerator.clearMetadata(JobGenerator.scala:257)
> 	at org.apache.spark.streaming.scheduler.JobGenerator.org$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:178)
> 	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)
> {quote}
> The code is very simple: 
> {code}
>         val sc = new SparkContext(conf)
>         val sqlContext = new HiveContext(sc)
>         import sqlContext.implicits._
>         println(">>> create streamingContext.")
>         val ssc = new StreamingContext(sc, Seconds(1))
>         ssc.queueStream(
>             Queue(
>                 sc.makeRDD(Seq(1)), 
>                 sc.makeRDD(Seq[Int]()), 
>                 sc.makeRDD(Seq(2))
>                 ), true).transform(rdd => if (rdd.isEmpty()) rdd else null).print
>         ssc.start()
> {code}



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