Thanks RK. I can turn on speculative execution but I am trying to find out
actual reason for delay as it happens on any node. Any idea about the stack
trace in my previous mail.
Regards,Ajay
On Thursday, January 15, 2015 8:02 PM, RK <prk...@yahoo.com.INVALID> wrote:
If you don't want a few slow tasks to slow down the entire job, you can turn
on speculation.
Here are the speculation settings from Spark Configuration - Spark 1.2.0
Documentation.
| |
| | | | | |
| Spark Configuration - Spark 1.2.0 DocumentationSpark Configuration Spark
Properties Dynamically Loading Spark Properties Viewing Spark Properties
Available Properties Application Properties Runtime Environment Shuffle
Behavior Spark UI |
| |
| View on spark.apache.org | Preview by Yahoo |
| |
| |
| spark.speculation | false | If set to "true", performs speculative execution
of tasks. This means if one or more tasks are running slowly in a stage, they
will be re-launched. |
| spark.speculation.interval | 100 | How often Spark will check for tasks to
speculate, in milliseconds. |
| spark.speculation.quantile | 0.75 | Percentage of tasks which must be
complete before speculation is enabled for a particular stage. |
| spark.speculation.multiplier | 1.5 | How many times slower a task is than the
median to be considered for speculation.
|
On Thursday, January 15, 2015 5:44 AM, Ajay Srivastava
<a_k_srivast...@yahoo.com.INVALID> wrote:
Hi,
My spark job is taking long time. I see that some tasks are taking longer time
for same amount of data and shuffle read/write. What could be the possible
reasons for it ?
The thread-dump sometimes show that all the tasks in an executor are waiting
with following stack trace -
"Executor task launch worker-12" daemon prio=10 tid=0x00007fcd44276000
nid=0x3f85 waiting on condition [0x00007fcce3ddc000]
java.lang.Thread.State: WAITING (parking)
at sun.misc.Unsafe.park(Native Method)
- parking to wait for <0x00007fd0aee82e00> (a
java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
at java.util.concurrent.locks.LockSupport.park(Unknown Source)
at
java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(Unknown
Source)
at java.util.concurrent.LinkedBlockingQueue.take(Unknown Source)
at
org.apache.spark.storage.BlockFetcherIterator$BasicBlockFetcherIterator.next(BlockFetcherIterator.scala:253)
at
org.apache.spark.storage.BlockFetcherIterator$BasicBlockFetcherIterator.next(BlockFetcherIterator.scala:77)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at
org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:30)
at
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at
org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:137)
at
org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:159)
at
org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:158)
at
scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at
scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)
at org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:158)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
at org.apache.spark.rdd.FilteredRDD.compute(FilteredRDD.scala:34)
Any inputs/suggestions to improve job time will be appreciated.
Regards,Ajay
