Hi Ankur If your hardware is ok, looks like it is config problem. Can you show me the config of spark-env.sh or JVM config?
Thanks Wisely Chen 2015-03-28 15:39 GMT+08:00 Ankur Srivastava <ankur.srivast...@gmail.com>: > Hi Wisely, > I have 26gb for driver and the master is running on m3.2xlarge machines. > > I see OOM errors on workers and even they are running with 26th of memory. > > Thanks > > On Fri, Mar 27, 2015, 11:43 PM Wisely Chen <wiselyc...@appier.com> wrote: > >> Hi >> >> In broadcast, spark will collect the whole 3gb object into master node >> and broadcast to each slaves. It is very common situation that the master >> node don't have enough memory . >> >> What is your master node settings? >> >> Wisely Chen >> >> Ankur Srivastava <ankur.srivast...@gmail.com> 於 2015年3月28日 星期六寫道： >> >> I have increased the "spark.storage.memoryFraction" to 0.4 but I still >>> get OOM errors on Spark Executor nodes >>> >>> >>> 15/03/27 23:19:51 INFO BlockManagerMaster: Updated info of block >>> broadcast_5_piece10 >>> >>> 15/03/27 23:19:51 INFO TorrentBroadcast: Reading broadcast variable 5 >>> took 2704 ms >>> >>> 15/03/27 23:19:52 INFO MemoryStore: ensureFreeSpace(672530208) called >>> with curMem=2484698683, maxMem=9631778734 >>> >>> 15/03/27 23:19:52 INFO MemoryStore: Block broadcast_5 stored as values >>> in memory (estimated size 641.4 MB, free 6.0 GB) >>> >>> 15/03/27 23:34:02 WARN AkkaUtils: Error sending message in 1 attempts >>> >>> java.util.concurrent.TimeoutException: Futures timed out after [30 >>> seconds] >>> >>> at >>> scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219) >>> >>> at >>> scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223) >>> >>> at >>> scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107) >>> >>> at >>> scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53) >>> >>> at scala.concurrent.Await$.result(package.scala:107) >>> >>> at >>> org.apache.spark.util.AkkaUtils$.askWithReply(AkkaUtils.scala:187) >>> >>> at >>> org.apache.spark.executor.Executor$$anon$1.run(Executor.scala:407) >>> >>> 15/03/27 23:34:02 ERROR Executor: Exception in task 7.0 in stage 2.0 >>> (TID 4007) >>> >>> java.lang.OutOfMemoryError: GC overhead limit exceeded >>> >>> at >>> java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1986) >>> >>> at >>> java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1915) >>> >>> at >>> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798) >>> >>> Thanks >>> >>> Ankur >>> >>> On Fri, Mar 27, 2015 at 2:52 PM, Ankur Srivastava < >>> ankur.srivast...@gmail.com> wrote: >>> >>>> Hi All, >>>> >>>> I am running a spark cluster on EC2 instances of type: m3.2xlarge. I >>>> have given 26gb of memory with all 8 cores to my executors. I can see that >>>> in the logs too: >>>> >>>> *15/03/27 21:31:06 INFO AppClient$ClientActor: Executor added: >>>> app-20150327213106-0000/0 on worker-20150327212934-10.x.y.z-40128 >>>> (10.x.y.z:40128) with 8 cores* >>>> >>>> I am not caching any RDD so I have set "spark.storage.memoryFraction" >>>> to 0.2. I can see on SparkUI under executors tab Memory used is 0.0/4.5 GB. >>>> >>>> I am now confused with these logs? >>>> >>>> *15/03/27 21:31:08 INFO BlockManagerMasterActor: Registering block >>>> manager 10.77.100.196:58407 <http://10.77.100.196:58407> with 4.5 GB RAM, >>>> BlockManagerId(4, 10.x.y.z, 58407)* >>>> >>>> I am broadcasting a large object of 3 gb and after that when I am >>>> creating an RDD, I see logs which show this 4.5 GB memory getting full and >>>> then I get OOM. >>>> >>>> How can I make block manager use more memory? >>>> >>>> Is there any other fine tuning I need to do for broadcasting large >>>> objects? >>>> >>>> And does broadcast variable use cache memory or rest of the heap? >>>> >>>> >>>> Thanks >>>> >>>> Ankur >>>> >>> >>>
