Spark streaming: StorageLevel.MEMORY_AND_DISK_SER setting for KafkaUtils.createDirectStream

[Vinti Maheshwari]

Hi All,

I wanted to set *StorageLevel.MEMORY_AND_DISK_SER* in my spark-streaming
program as currently i am getting
MetadataFetchFailedException*. *I am not sure where i should pass
StorageLevel.MEMORY_AND_DISK, as it seems like createDirectStream doesn't
allow to pass that parameter.


val messages = KafkaUtils.createDirectStream[String, String,
StringDecoder, StringDecoder](
  ssc, kafkaParams, topicsSet)


Full Error:

*org.apache.spark.shuffle.MetadataFetchFailedException: Missing an output
location for shuffle 0*
    at
org.apache.spark.MapOutputTracker$$anonfun$org$apache$spark$MapOutputTracker$$convertMapStatuses$2.apply(MapOutputTracker.scala:460)
    at
org.apache.spark.MapOutputTracker$$anonfun$org$apache$spark$MapOutputTracker$$convertMapStatuses$2.apply(MapOutputTracker.scala:456)
    at
scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772)
    at
scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
    at
scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)
    at
org.apache.spark.MapOutputTracker$.org$apache$spark$MapOutputTracker$$convertMapStatuses(MapOutputTracker.scala:456)
    at
org.apache.spark.MapOutputTracker.getMapSizesByExecutorId(MapOutputTracker.scala:183)
    at
org.apache.spark.shuffle.hash.HashShuffleReader.read(HashShuffleReader.scala:47)
    at org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:90)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
    at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
    at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:262)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
    at org.apache.spark.scheduler.Task.run(Task.scala:88)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
    at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
    at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
    at java.lang.Thread.run(Thread.java:745)

)

Thanks,
~Vinti