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$1.run(Executor.scala:407) > > 15/03/27 23:34:02 ERROR Executor: Exception in task 7.0 in stage 2.0 (TID > 4007) > > java.lang.OutOfMemoryError: GC overhead limit exceeded > > at > java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1986) > > at > java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1915) > > at > java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798) > > Thanks > > Ankur > > On Fri, Mar 27, 2015 at 2:52 PM, Ankur Srivastava < > ankur.srivast...@gmail.com > <javascript:_e(%7B%7D,'cvml','ankur.srivast...@gmail.com');>> wrote: > >> Hi All, >> >> I am running a spark cluster on EC2 instances of type: m3.2xlarge. I have >> given 26gb of memory with all 8 cores to my executors. I can see that in >> the logs too: >> >> *15/03/27 21:31:06 INFO AppClient$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 >> > >
