Thanks Shixiong. Sure. Please find the details:
Spark-version: 1.5.2
I am doing data aggregation using check pointing, not sure if this is
causing issue.
Also, i am using perl_kafka producer to push data to kafka and then my
spark program is reading it from kafka. Not sure, if i need to use
createStream function instead of createDirectStream?
My program:
def main(args: Array[String]): Unit = {
val checkpointDirectory = "hdfs://<hostname>:8020/user/spark/" + args(2)
// Function to create and setup a new StreamingContext
def functionToCreateContext(): StreamingContext = {
val conf = new SparkConf().setAppName("HBaseStream")
val sc = new SparkContext(conf)
// create a StreamingContext, the main entry point for all
streaming functionality
val ssc = new StreamingContext(sc, Seconds(1))
val brokers = args(0)
val topics= args(1)
val topicsSet = topics.split(",").toSet
val kafkaParams = Map[String, String]("metadata.broker.list" ->
brokers, "auto.offset.reset" -> "smallest")
val messages = KafkaUtils.createDirectStream[String, String,
StringDecoder, StringDecoder](
ssc, kafkaParams, topicsSet)
// parse the lines of data into coverage objects
val inputStream = messages.map(_._2)
ssc.checkpoint(checkpointDirectory)
inputStream.print(1)
val parsedStream = inputStream
.map(line => {
val splitLines = line.split(",")
(splitLines(1), splitLines.slice(2,
splitLines.length).map((_.trim.toLong)))
})
import breeze.linalg.{DenseVector => BDV}
import scala.util.Try
val state: DStream[(String, Array[Long])] = parsedStream.updateStateByKey(
(current: Seq[Array[Long]], prev: Option[Array[Long]]) => {
prev.map(_ +: current).orElse(Some(current))
.flatMap(as => Try(as.map(BDV(_)).reduce(_ + _).toArray).toOption)
})
state.checkpoint(Duration(10000))
state.foreachRDD(rdd => rdd.foreach(Blaher.blah))
ssc
}
val context = StreamingContext.getOrCreate(checkpointDirectory,
functionToCreateContext _)
context.start()
context.awaitTermination()
}
}
Thanks & Regards,
Vinti
Thanks & Regards,
Vinti
On Wed, Mar 2, 2016 at 10:28 AM, Shixiong(Ryan) Zhu <shixi...@databricks.com
> wrote:
> Hey,
>
> KafkaUtils.createDirectStream doesn't need a StorageLevel as it doesn't
> store blocks to BlockManager. However, the error is not related
> to StorageLevel. It may be a bug. Could you provide more info about it?
> E.g., Spark version, your codes, logs.
>
> On Wed, Mar 2, 2016 at 3:02 AM, 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
>>
>
>
