spark-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Antony Mayi <antonym...@yahoo.com.INVALID>
Subject Re: java.lang.OutOfMemoryError: GC overhead limit exceeded
Date Tue, 27 Jan 2015 23:24:28 GMT
I have yarn configured with yarn.nodemanager.vmem-check-enabled=false and yarn.nodemanager.pmem-check-enabled=false
to avoid yarn killing the containers.
the stack trace is bellow.
thanks,Antony.
15/01/27 17:02:53 ERROR executor.CoarseGrainedExecutorBackend: RECEIVED SIGNAL 15: SIGTERM15/01/27
17:02:53 ERROR executor.Executor: Exception in task 21.0 in stage 12.0 (TID 1312)java.lang.OutOfMemoryError:
GC overhead limit exceeded        at java.lang.Integer.valueOf(Integer.java:642)   
    at scala.runtime.BoxesRunTime.boxToInteger(BoxesRunTime.java:70)        at scala.collection.mutable.ArrayOps$ofInt.apply(ArrayOps.scala:156) 
      at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) 
      at scala.collection.mutable.ArrayOps$ofInt.foreach(ArrayOps.scala:156)       
at scala.collection.SeqLike$class.distinct(SeqLike.scala:493)        at scala.collection.mutable.ArrayOps$ofInt.distinct(ArrayOps.scala:156) 
      at org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeOutLinkBlock(ALS.scala:404) 
      at org.apache.spark.mllib.recommendation.ALS$$anonfun$15.apply(ALS.scala:459)   
    at org.apache.spark.mllib.recommendation.ALS$$anonfun$15.apply(ALS.scala:456)     
  at org.apache.spark.rdd.RDD$$anonfun$14.apply(RDD.scala:614)        at org.apache.spark.rdd.RDD$$anonfun$14.apply(RDD.scala:614) 
      at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)   
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)        at org.apache.spark.rdd.RDD.iterator(RDD.scala:230) 
      at org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31)     
  at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)        at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:61) 
      at org.apache.spark.rdd.RDD.iterator(RDD.scala:228)        at org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$2.apply(CoGroupedRDD.scala:130) 
      at org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$2.apply(CoGroupedRDD.scala:127) 
      at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772) 
      at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) 
      at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)       
at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)     
  at org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:127)        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263) 
      at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)        at org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31) 
      at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)        at
org.apache.spark.rdd.RDD.iterator(RDD.scala:230)        at org.apache.spark.rdd.FlatMappedValuesRDD.compute(FlatMappedValuesRDD.scala:31)15/01/27
17:02:53 ERROR util.SparkUncaughtExceptionHandler: Uncaught exception in thread Thread[Executor
task launch worker-8,5,main]java.lang.OutOfMemoryError: GC overhead limit exceeded     
  at java.lang.Integer.valueOf(Integer.java:642)        at scala.runtime.BoxesRunTime.boxToInteger(BoxesRunTime.java:70) 
      at scala.collection.mutable.ArrayOps$ofInt.apply(ArrayOps.scala:156)        at
scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)       
at scala.collection.mutable.ArrayOps$ofInt.foreach(ArrayOps.scala:156)        at scala.collection.SeqLike$class.distinct(SeqLike.scala:493) 
      at scala.collection.mutable.ArrayOps$ofInt.distinct(ArrayOps.scala:156)       
at org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeOutLinkBlock(ALS.scala:404) 
      at org.apache.spark.mllib.recommendation.ALS$$anonfun$15.apply(ALS.scala:459)   
    at org.apache.spark.mllib.recommendation.ALS$$anonfun$15.apply(ALS.scala:456)     
  at org.apache.spark.rdd.RDD$$anonfun$14.apply(RDD.scala:614)        at org.apache.spark.rdd.RDD$$anonfun$14.apply(RDD.scala:614) 
      at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)   
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)        at org.apache.spark.rdd.RDD.iterator(RDD.scala:230) 
      at org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31)     
  at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)        at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:61) 
      at org.apache.spark.rdd.RDD.iterator(RDD.scala:228)        at org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$2.apply(CoGroupedRDD.scala:130) 
      at org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$2.apply(CoGroupedRDD.scala:127) 
      at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772) 
      at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) 
      at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)       
at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)     
  at org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:127)        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263) 
      at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)        at org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31) 
      at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)        at
org.apache.spark.rdd.RDD.iterator(RDD.scala:230)        at org.apache.spark.rdd.FlatMappedValuesRDD.compute(FlatMappedValuesRDD.scala:31)
 

     On Wednesday, 28 January 2015, 0:01, Guru Medasani <gdmeda@outlook.com> wrote:
   
 

 Can you attach the logs where this is failing?
From:  Sven Krasser <krasser@gmail.com>
Date:  Tuesday, January 27, 2015 at 4:50 PM
To:  Guru Medasani <gdmeda@outlook.com>
Cc:  Sandy Ryza <sandy.ryza@cloudera.com>, Antony Mayi <antonymayi@yahoo.com>,
"user@spark.apache.org" <user@spark.apache.org>
Subject:  Re: java.lang.OutOfMemoryError: GC overhead limit exceeded

Since it's an executor running OOM it doesn't look like a container being killed by YARN to
me. As a starting point, can you repartition your job into smaller tasks?
-Sven

On Tue, Jan 27, 2015 at 2:34 PM, Guru Medasani <gdmeda@outlook.com> wrote:

Hi Anthony,
What is the setting of the total amount of memory in MB that can be allocated to containers
on your NodeManagers?
yarn.nodemanager.resource.memory-mb
Can you check this above configuration in yarn-site.xml used by the node manager process?
-Guru Medasani
From:  Sandy Ryza <sandy.ryza@cloudera.com>
Date:  Tuesday, January 27, 2015 at 3:33 PM
To:  Antony Mayi <antonymayi@yahoo.com>
Cc:  "user@spark.apache.org" <user@spark.apache.org>
Subject:  Re: java.lang.OutOfMemoryError: GC overhead limit exceeded

Hi Antony,
If you look in the YARN NodeManager logs, do you see that it's killing the executors?  Or
are they crashing for a different reason?
-Sandy
On Tue, Jan 27, 2015 at 12:43 PM, Antony Mayi <antonymayi@yahoo.com.invalid> wrote:

Hi,
I am using spark.yarn.executor.memoryOverhead=8192 yet getting executors crashed with this
error.
does that mean I have genuinely not enough RAM or is this matter of config tuning?
other config options used:spark.storage.memoryFraction=0.3
SPARK_EXECUTOR_MEMORY=14G
running spark 1.2.0 as yarn-client on cluster of 10 nodes (the workload is ALS trainImplicit
on ~15GB dataset)
thanks for any ideas,Antony.





-- 
http://sites.google.com/site/krasser/?utm_source=sig


 
   
Mime
View raw message