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From Nicos <n...@hotmail.com>
Subject Re: Some tasks are taking long time
Date Thu, 15 Jan 2015 18:24:20 GMT
Ajay,
	Unless we are dealing with some synchronization/conditional variable bug in Spark, try this
per tuning guide:
Cache Size Tuning

One important configuration parameter for GC is the amount of memory that should be used for
caching RDDs. By default, Spark uses 60% of the configured executor memory (spark.executor.memory)
to cache RDDs. This means that 40% of memory is available for any objects created during task
execution.

In case your tasks slow down and you find that your JVM is garbage-collecting frequently or
running out of memory, lowering this value will help reduce the memory consumption. To change
this to, say, 50%, you can call conf.set("spark.storage.memoryFraction", "0.5") on your SparkConf.
Combined with the use of serialized caching, using a smaller cache should be sufficient to
mitigate most of the garbage collection problems. In case you are interested in further tuning
the Java GC, continue reading below.


Complete list of tips here:
https://spark.apache.org/docs/latest/tuning.html#serialized-rdd-storage <https://spark.apache.org/docs/latest/tuning.html#serialized-rdd-storage>

Cheers,
- Nicos

> On Jan 15, 2015, at 6:49 AM, Ajay Srivastava <a_k_srivastava@yahoo.com.INVALID>
wrote:
> 
> 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 <prk001@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
<http://spark.apache.org/docs/1.2.0/configuration.html>.
>  
>  
>  
>  
>  
>  
> Spark Configuration - Spark 1.2.0 Documentation
>  <http://spark.apache.org/docs/1.2.0/configuration.html>Spark 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 <http://spark.apache.org/docs/1.2.0/configuration.html>

> 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_srivastava@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
> 
> 
> 
> 
> 
> 


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