Can you check your local and remote logs?

2015-05-06 16:24 GMT-04:00 Wang, Ningjun (LNG-NPV) <
ningjun.w...@lexisnexis.com>:

>  This problem happen in Spark 1.3.1.  It happen when two jobs are running
> simultaneously each in its own Spark Context.
>
>
>
> I don’t remember seeing this bug in Spark 1.2.0. Is it a new bug
> introduced in Spark 1.3.1?
>
>
>
> Ningjun
>
>
>
> *From:* Ted Yu [mailto:yuzhih...@gmail.com]
> *Sent:* Wednesday, May 06, 2015 11:32 AM
> *To:* Wang, Ningjun (LNG-NPV)
> *Cc:* user@spark.apache.org
> *Subject:* Re: java.io.IOException: org.apache.spark.SparkException:
> Failed to get broadcast_2_piece0
>
>
>
> Which release of Spark are you using ?
>
>
>
> Thanks
>
>
> On May 6, 2015, at 8:03 AM, Wang, Ningjun (LNG-NPV) <
> ningjun.w...@lexisnexis.com> wrote:
>
>  I run a job on spark standalone cluster and got the exception below
>
>
>
> Here is the line of code that cause problem
>
>
>
> *val *myRdd: RDD[(String, String, String)] = … *// RDD of (docid,
> cattegory, path) *
>
>
> myRdd.persist(StorageLevel.*MEMORY_AND_DISK_SER*)
>
> *val *cats: Array[String] = myRdd.map(t => t._2).distinct().collect()  //
> This line cause the exception
>
>
>
>
>
> 15/05/06 10:48:51 WARN TaskSetManager: Lost task 0.0 in stage 0.0 (TID 0,
> LAB4-WIN03.pcc.lexisnexis.com): java.io.IOException: 
> org.apache.spark.SparkException:
> Failed to get broadcast_2_piece0 of broadcast_2
>
>         at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1156)
>
>         at
> org.apache.spark.broadcast.TorrentBroadcast.readBroadcastBlock(TorrentBroadcast.scala:164)
>
>         at
> org.apache.spark.broadcast.TorrentBroadcast._value$lzycompute(TorrentBroadcast.scala:64)
>
>         at
> org.apache.spark.broadcast.TorrentBroadcast._value(TorrentBroadcast.scala:64)
>
>         at
> org.apache.spark.broadcast.TorrentBroadcast.getValue(TorrentBroadcast.scala:87)
>
>         at org.apache.spark.broadcast.Broadcast.value(Broadcast.scala:70)
>
>         at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:61)
>
>         at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
>
>         at org.apache.spark.scheduler.Task.run(Task.scala:64)
>
>         at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:203)
>
>         at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>
>         at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>
>         at java.lang.Thread.run(Thread.java:745)
>
> Caused by: org.apache.spark.SparkException: Failed to get
> broadcast_2_piece0 of broadcast_2
>
>         at
> org.apache.spark.broadcast.TorrentBroadcast$$anonfun$org$apache$spark$broadcast$TorrentBr
>
> oadcast$$readBlocks$1$$anonfun$2.apply(TorrentBroadcast.scala:137)
>
>         at
> org.apache.spark.broadcast.TorrentBroadcast$$anonfun$org$apache$spark$broadcast$TorrentBr
>
> oadcast$$readBlocks$1$$anonfun$2.apply(TorrentBroadcast.scala:137)
>
>         at scala.Option.getOrElse(Option.scala:120)
>
>         at
> org.apache.spark.broadcast.TorrentBroadcast$$anonfun$org$apache$spark$broadcast$TorrentBr
>
> oadcast$$readBlocks$1.apply$mcVI$sp(TorrentBroadcast.scala:136)
>
>         at
> org.apache.spark.broadcast.TorrentBroadcast$$anonfun$org$apache$spark$broadcast$TorrentBr
>
> oadcast$$readBlocks$1.apply(TorrentBroadcast.scala:119)
>
>         at
> org.apache.spark.broadcast.TorrentBroadcast$$anonfun$org$apache$spark$broadcast$TorrentBr
>
> oadcast$$readBlocks$1.apply(TorrentBroadcast.scala:119)
>
>         at scala.collection.immutable.List.foreach(List.scala:318)
>
>         at org.apache.spark.broadcast.TorrentBroadcast.org
> $apache$spark$broadcast$TorrentBroadcast$$
>
> readBlocks(TorrentBroadcast.scala:119)
>
>         at
> org.apache.spark.broadcast.TorrentBroadcast$$anonfun$readBroadcastBlock$1.apply(TorrentBr
>
> oadcast.scala:174)
>
>         at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1153)
>
>         ... 12 more
>
>
>
>
>
> Any idea what cause the problem and how to avoid it?
>
>
>
> Thanks
>
> Ningjun
>
>
>
>

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