Hi Ankur If you using standalone mode, your config is wrong. You should use "export SPARK_DAEMON_MEMORY=xxx " in config/spark-env.sh. At least it works on my spark 1.3.0 standalone mode machine.
BTW, The SPARK_DRIVER_MEMORY is used in Yarn mode and looks like the standalone mode don't use this config. To debug this, please type "ps auxw | grep org.apache.spark.deploy.master.[M]aster" in master machine. You can see the Xmx and Xms option. Wisely Chen On Mon, Mar 30, 2015 at 3:55 AM, Ankur Srivastava < ankur.srivast...@gmail.com> wrote: > Hi Wisely, > > I am running on Amazon EC2 instances so I can not doubt the hardware. > Moreover my other pipelines run successfully except for this which involves > Broadcasting large object. > > My spark-en.sh setting are: > > SPARK_MASTER_IP=<MASTER-IP> > > SPARK_LOCAL_IP=<LOCAL-IP> > > SPARK_DRIVER_MEMORY=24g > > SPARK_WORKER_MEMORY=28g > > SPARK_EXECUTOR_MEMORY=26g > > SPARK_WORKER_CORES=8 > > My spark-default.sh settings are: > > spark.eventLog.enabled true > > spark.eventLog.dir /srv/logs/ > > spark.serializer org.apache.spark.serializer.KryoSerializer > > spark.kryo.registrator > com.test.utils.KryoSerializationRegistrator > > spark.executor.extraJavaOptions "-verbose:gc -XX:+PrintGCDetails > -XX:+PrintGCTimeStamps -XX:+HeapDumpOnOutOfMemoryError > -XX:HeapDumpPath=/srv/logs/ -XX:+UseG1GC" > > spark.shuffle.consolidateFiles true > > spark.shuffle.manager sort > > spark.shuffle.compress true > > spark.rdd.compress true > Thanks > Ankur > > On Sat, Mar 28, 2015 at 7:57 AM, Wisely Chen <wiselyc...@appier.com> > wrote: > >> 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 >>>>>> >>>>> >>>>> >> >
