Failed to get broadcast_1_piece0 of broadcast_1 in Spark Streaming job - hadoop

Question

I am running spark jobs on yarn in cluster mode. The job get the messages from kafka direct stream. I am using broadcast variables and checkpointing every 30 seconds. When I start the job first time it runs fine without any issue. If I kill the job and restart it throws below exception in executor upon receiving a message from kafka:
java.io.IOException: org.apache.spark.SparkException: Failed to get broadcast_1_piece0 of broadcast_1
at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1178)
at org.apache.spark.broadcast.TorrentBroadcast.readBroadcastBlock(TorrentBroadcast.scala:165)
at org.apache.spark.broadcast.TorrentBroadcast._value$lzycompute(TorrentBroadcast.scala:64)
at org.apache.spark.broadcast.TorrentBroadcast._value(TorrentBroadcast.scala:64)
at org.apache.spark.broadcast.TorrentBroadcast.getValue(TorrentBroadcast.scala:88)
at org.apache.spark.broadcast.Broadcast.value(Broadcast.scala:70)
at net.juniper.spark.stream.LogDataStreamProcessor$2.call(LogDataStreamProcessor.java:177)
at net.juniper.spark.stream.LogDataStreamProcessor$2.call(LogDataStreamProcessor.java:1)
at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$fn$1$1.apply(JavaDStreamLike.scala:172)
at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$fn$1$1.apply(JavaDStreamLike.scala:172)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at scala.collection.Iterator$$anon$14.hasNext(Iterator.scala:388)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
at scala.collection.AbstractIterator.to(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1298)
at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1298)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1850)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1850)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:88)
at org.apache.spark.executor.Executor$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)
Does anyone have idea how to resolve this error?
Spark version: 1.5.0
CDH 5.5.1

Answer 1

When encountering issues where only the first run works, it always resulted in issues revolving the checkpoint data. Moreover, the use of checkpoints only happens when there is something to check, which is the first message from kafka.
I suggest you check if you the job is indeed dead, that is, maybe the process is still running on the machine that executed it.
try running a simple ps -fe and see if something is still running. if there are 2 processes trying to use the same checkpoint folder, it will always fail.
hope this helps