Thanks Nicos.GC does not contribute much to the execution time of the task. I
will debug it further today.
Regards,Ajay
On Thursday, January 15, 2015 11:55 PM, Nicos <n...@hotmail.com> wrote:
Ajay, Unless we are dealing with some synchronization/conditional variable bug
in Spark, try this per tuning guide:Cache Size TuningOne 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
Cheers,- Nicos
On Jan 15, 2015, at 6:49 AM, Ajay Srivastava <a_k_srivast...@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 <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
