Abnormally large deserialisation time for some tasks

[Abhishek Modi <ab...@gmail.com>]

I'm doing a mapPartitions on a rdd cached in memory followed by a reduce.
Here is my code snippet

// myRdd is an rdd consisting of Tuple2[Int,Long]
myRdd.mapPartitions(rangify).reduce( (x,y) => (x._1+y._1,x._2 ++ y._2))

//The rangify function
def rangify(l: Iterator[ Tuple2[Int,Long] ]) : Iterator[ Tuple2[Long, List [
ArrayBuffer[ Tuple2[Long,Long] ] ] ] ]= {
  var sum=0L
  val mylist=ArrayBuffer[ Tuple2[Long,Long] ]()

  if(l.isEmpty)
    return List( (0L,List [ ArrayBuffer[ Tuple2[Long,Long] ] ]
())).toIterator

  var prev= -1000L
  var begin= -1000L

  for (x <- l){
    sum+=x._1

    if(prev<0){
      prev=x._2
      begin=x._2
    }

    else if(x._2==prev+1)
      prev=x._2

    else {
      list+=((begin,prev))
      prev=x._2
      begin=x._2
    }
  }

  mylist+= ((begin,prev))

  List((sum, List(mylist) ) ).toIterator
}


The rdd is cached in memory. I'm using 20 executors with 1 core for each
executor. The cached rdd has 60 blocks. The problem is for every 2-3 runs of
the job, there is a task which has an abnormally large deserialisation time.
Screenshot attached

<http://apache-spark-user-list.1001560.n3.nabble.com/file/n26233/chart.png> 

<http://apache-spark-user-list.1001560.n3.nabble.com/file/n26233/task_metrics.png> 

What could be the possible reason for this ?



--
View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Abnormally-large-deserialisation-time-for-some-tasks-tp26233.html
Sent from the Apache Spark User List mailing list archive at Nabble.com.

---------------------------------------------------------------------
To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
For additional commands, e-mail: user-help@spark.apache.org