Thanks much Saisai. Got it. So i think increasing worker executor memory might work. Trying that.
Regards, ~Vinti On Wed, Mar 2, 2016 at 4:21 AM, Saisai Shao <sai.sai.s...@gmail.com> wrote: > You don't have to specify the storage level for direct Kafka API, since it > doesn't require to store the input data ahead of time. Only receiver-based > approach could specify the storage level. > > Thanks > Saisai > > On Wed, Mar 2, 2016 at 7:08 PM, Vinti Maheshwari <vinti.u...@gmail.com> > wrote: > >> 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 >> >> >
