Thanks for sticking to this thread. I am guessing what memory my app requests and what Yarn requests on my part should be same and is determined by the value of *--executor-memory* which I had set to *20G*. Or can the two values be different?
I checked in Yarn configurations(below), so I think that fits well into the memory overhead limits. Container Memory Maximum yarn.scheduler.maximum-allocation-mb MiBGiB Reset to the default value: 64 GiB <http://10.1.1.49:7180/cmf/services/108/config#> Override Instances <http://10.1.1.49:7180/cmf/service/108/roleType/RESOURCEMANAGER/group/yarn-RESOURCEMANAGER-BASE/config/yarn_scheduler_maximum_allocation_mb?wizardMode=false&returnUrl=%2Fcmf%2Fservices%2F108%2Fconfig&filterValue=> The largest amount of physical memory, in MiB, that can be requested for a container. On Thu, Jan 15, 2015 at 10:28 AM, Sean Owen <so...@cloudera.com> wrote: > Those settings aren't relevant, I think. You're concerned with what > your app requests, and what Spark requests of YARN on your behalf. (Of > course, you can't request more than what your cluster allows for a > YARN container for example, but that doesn't seem to be what is > happening here.) > > You do not want to omit --executor-memory if you need large executor > memory heaps, since then you just request the default and that is > evidently not enough memory for your app. > > Look at http://spark.apache.org/docs/latest/running-on-yarn.html and > spark.yarn.executor.memoryOverhead By default it's 7% of your 20G or > about 1.4G. You might set this higher to 2G to give more overhead. > > See the --config property=value syntax documented in > http://spark.apache.org/docs/latest/submitting-applications.html > > On Thu, Jan 15, 2015 at 3:47 AM, Nitin kak <nitinkak...@gmail.com> wrote: > > Thanks Sean. > > > > I guess Cloudera Manager has parameters executor_total_max_heapsize and > > worker_max_heapsize which point to the parameters you mentioned above. > > > > How much should that cushon between the jvm heap size and yarn memory > limit > > be? > > > > I tried setting jvm memory to 20g and yarn to 24g, but it gave the same > > error as above. > > > > Then, I removed the "--executor-memory" clause > > > > spark-submit --class ConnectedComponentsTest --master yarn-cluster > > --num-executors 7 --executor-cores 1 > > target/scala-2.10/connectedcomponentstest_2.10-1.0.jar > > > > That is not giving GC, Out of memory exception > > > > 15/01/14 21:20:33 WARN channel.DefaultChannelPipeline: An exception was > > thrown by a user handler while handling an exception event ([id: > 0x362d65d4, > > /10.1.1.33:35463 => /10.1.1.73:43389] EXCEPTION: > java.lang.OutOfMemoryError: > > GC overhead limit exceeded) > > java.lang.OutOfMemoryError: GC overhead limit exceeded > > at java.lang.Object.clone(Native Method) > > at akka.util.CompactByteString$.apply(ByteString.scala:410) > > at akka.util.ByteString$.apply(ByteString.scala:22) > > at > > > akka.remote.transport.netty.TcpHandlers$class.onMessage(TcpSupport.scala:45) > > at > > > akka.remote.transport.netty.TcpServerHandler.onMessage(TcpSupport.scala:57) > > at > > > akka.remote.transport.netty.NettyServerHelpers$class.messageReceived(NettyHelpers.scala:43) > > at > > > akka.remote.transport.netty.ServerHandler.messageReceived(NettyTransport.scala:179) > > at > org.jboss.netty.channel.Channels.fireMessageReceived(Channels.java:296) > > at > > > org.jboss.netty.handler.codec.frame.FrameDecoder.unfoldAndFireMessageReceived(FrameDecoder.java:462) > > at > > > org.jboss.netty.handler.codec.frame.FrameDecoder.callDecode(FrameDecoder.java:443) > > at > > > org.jboss.netty.handler.codec.frame.FrameDecoder.messageReceived(FrameDecoder.java:303) > > at > org.jboss.netty.channel.Channels.fireMessageReceived(Channels.java:268) > > at > org.jboss.netty.channel.Channels.fireMessageReceived(Channels.java:255) > > at > org.jboss.netty.channel.socket.nio.NioWorker.read(NioWorker.java:88) > > at > > > org.jboss.netty.channel.socket.nio.AbstractNioWorker.process(AbstractNioWorker.java:109) > > at > > > org.jboss.netty.channel.socket.nio.AbstractNioSelector.run(AbstractNioSelector.java:312) > > at > > > org.jboss.netty.channel.socket.nio.AbstractNioWorker.run(AbstractNioWorker.java:90) > > at > org.jboss.netty.channel.socket.nio.NioWorker.run(NioWorker.java:178) > > at > > > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) > > at > > > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) > > at java.lang.Thread.run(Thread.java:745) > > 15/01/14 21:20:33 ERROR util.Utils: Uncaught exception in thread > > SparkListenerBus > > java.lang.OutOfMemoryError: GC overhead limit exceeded > > at > scala.collection.mutable.ListBuffer.$plus$eq(ListBuffer.scala:168) > > at > scala.collection.mutable.ListBuffer.$plus$eq(ListBuffer.scala:45) > > at > > > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) > > at > > > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) > > at scala.collection.immutable.List.foreach(List.scala:318) > > at > scala.collection.TraversableLike$class.map(TraversableLike.scala:244) > > at scala.collection.AbstractTraversable.map(Traversable.scala:105) > > at org.json4s.JsonDSL$class.seq2jvalue(JsonDSL.scala:68) > > at org.json4s.JsonDSL$.seq2jvalue(JsonDSL.scala:61) > > at > > > org.apache.spark.util.JsonProtocol$$anonfun$jobStartToJson$3.apply(JsonProtocol.scala:127) > > at > > > org.apache.spark.util.JsonProtocol$$anonfun$jobStartToJson$3.apply(JsonProtocol.scala:127) > > at org.json4s.JsonDSL$class.pair2jvalue(JsonDSL.scala:79) > > at org.json4s.JsonDSL$.pair2jvalue(JsonDSL.scala:61) > > at > > > org.apache.spark.util.JsonProtocol$.jobStartToJson(JsonProtocol.scala:127) > > at > > > org.apache.spark.util.JsonProtocol$.sparkEventToJson(JsonProtocol.scala:59) > > at > > > org.apache.spark.scheduler.EventLoggingListener.logEvent(EventLoggingListener.scala:92) > > at > > > org.apache.spark.scheduler.EventLoggingListener.onJobStart(EventLoggingListener.scala:118) > > at > > > org.apache.spark.scheduler.SparkListenerBus$$anonfun$postToAll$3.apply(SparkListenerBus.scala:50) > > at > > > org.apache.spark.scheduler.SparkListenerBus$$anonfun$postToAll$3.apply(SparkListenerBus.scala:50) > > at > > > org.apache.spark.scheduler.SparkListenerBus$$anonfun$foreachListener$1.apply(SparkListenerBus.scala:83) > > at > > > org.apache.spark.scheduler.SparkListenerBus$$anonfun$foreachListener$1.apply(SparkListenerBus.scala:81) > > at > > > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) > > at > scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) > > at > > > org.apache.spark.scheduler.SparkListenerBus$class.foreachListener(SparkListenerBus.scala:81) > > at > > > org.apache.spark.scheduler.SparkListenerBus$class.postToAll(SparkListenerBus.scala:50) > > at > > > org.apache.spark.scheduler.LiveListenerBus.postToAll(LiveListenerBus.scala:32) > > at > > > org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(LiveListenerBus.scala:56) > > at > > > org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(LiveListenerBus.scala:56) > > at scala.Option.foreach(Option.scala:236) > > at > > > org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1.apply$mcV$sp(LiveListenerBus.scala:56) > > at > > > org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1.apply(LiveListenerBus.scala:47) > > at > > > org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1.apply(LiveListenerBus.scala:47) > > Exception in thread "SparkListenerBus" java.lang.OutOfMemoryError: GC > > overhead limit exceeded > > at > scala.collection.mutable.ListBuffer.$plus$eq(ListBuffer.scala:168) > > at > scala.collection.mutable.ListBuffer.$plus$eq(ListBuffer.scala:45) > > at > > > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) > > at > > > scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) > > at scala.collection.immutable.List.foreach(List.scala:318) > > at > scala.collection.TraversableLike$class.map(TraversableLike.scala:244) > > at scala.collection.AbstractTraversable.map(Traversable.scala:105) > > at org.json4s.JsonDSL$class.seq2jvalue(JsonDSL.scala:68) > > at org.json4s.JsonDSL$.seq2jvalue(JsonDSL.scala:61) > > at > > > org.apache.spark.util.JsonProtocol$$anonfun$jobStartToJson$3.apply(JsonProtocol.scala:127) > > at > > > org.apache.spark.util.JsonProtocol$$anonfun$jobStartToJson$3.apply(JsonProtocol.scala:127) > > at org.json4s.JsonDSL$class.pair2jvalue(JsonDSL.scala:79) > > at org.json4s.JsonDSL$.pair2jvalue(JsonDSL.scala:61) > > at > > > org.apache.spark.util.JsonProtocol$.jobStartToJson(JsonProtocol.scala:127) > > at > > > org.apache.spark.util.JsonProtocol$.sparkEventToJson(JsonProtocol.scala:59) > > at > > > org.apache.spark.scheduler.EventLoggingListener.logEvent(EventLoggingListener.scala:92) > > at > > > org.apache.spark.scheduler.EventLoggingListener.onJobStart(EventLoggingListener.scala:118) > > at > > > org.apache.spark.scheduler.SparkListenerBus$$anonfun$postToAll$3.apply(SparkListenerBus.scala:50) > > at > > > org.apache.spark.scheduler.SparkListenerBus$$anonfun$postToAll$3.apply(SparkListenerBus.scala:50) > > at > > > org.apache.spark.scheduler.SparkListenerBus$$anonfun$foreachListener$1.apply(SparkListenerBus.scala:83) > > at > > > org.apache.spark.scheduler.SparkListenerBus$$anonfun$foreachListener$1.apply(SparkListenerBus.scala:81) > > at > > > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) > > at > scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) > > at > > > org.apache.spark.scheduler.SparkListenerBus$class.foreachListener(SparkListenerBus.scala:81) > > at > > > org.apache.spark.scheduler.SparkListenerBus$class.postToAll(SparkListenerBus.scala:50) > > at > > > org.apache.spark.scheduler.LiveListenerBus.postToAll(LiveListenerBus.scala:32) > > at > > > org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(LiveListenerBus.scala:56) > > at > > > org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(LiveListenerBus.scala:56) > > at scala.Option.foreach(Option.scala:236) > > at > > > org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1.apply$mcV$sp(LiveListenerBus.scala:56) > > at > > > org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1.apply(LiveListenerBus.scala:47) > > at > > > org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1.apply(LiveListenerBus.scala:47) > > > > > > On Wed, Jan 14, 2015 at 4:44 PM, Sean Owen <so...@cloudera.com> wrote: > >> > >> That's not quite what that error means. Spark is not out of memory. It > >> means that Spark is using more memory than it asked YARN for. That in > >> turn is because the default amount of cushion established between the > >> YARN allowed container size and the JVM heap size is too small. See > >> spark.yarn.executor.memoryOverhead in > >> http://spark.apache.org/docs/latest/running-on-yarn.html > >> > >> On Wed, Jan 14, 2015 at 9:18 PM, nitinkak001 <nitinkak...@gmail.com> > >> wrote: > >> > I am trying to run connected components algorithm in Spark. The graph > >> > has > >> > roughly 28M edges and 3.2M vertices. Here is the code I am using > >> > > >> > /val inputFile = > >> > > "/user/hive/warehouse/spark_poc.db/window_compare_output_text/000000_0" > >> > val conf = new SparkConf().setAppName("ConnectedComponentsTest") > >> > val sc = new SparkContext(conf) > >> > val graph = GraphLoader.edgeListFile(sc, inputFile, true, 7, > >> > StorageLevel.MEMORY_AND_DISK, StorageLevel.MEMORY_AND_DISK); > >> > graph.cache(); > >> > val cc = graph.connectedComponents(); > >> > graph.edges.saveAsTextFile("/user/kakn/output");/ > >> > > >> > and here is the command: > >> > > >> > /spark-submit --class ConnectedComponentsTest --master yarn-cluster > >> > --num-executors 7 --driver-memory 6g --executor-memory 8g > >> > --executor-cores 1 > >> > target/scala-2.10/connectedcomponentstest_2.10-1.0.jar/ > >> > > >> > It runs for about an hour and then fails with below error. *Isnt Spark > >> > supposed to spill on disk if the RDDs dont fit into the memory?* > >> > > >> > Application application_1418082773407_8587 failed 2 times due to AM > >> > Container for appattempt_1418082773407_8587_000002 exited with > exitCode: > >> > -104 due to: Container > >> > [pid=19790,containerID=container_1418082773407_8587_02_000001] is > >> > running > >> > beyond physical memory limits. Current usage: 6.5 GB of 6.5 GB > physical > >> > memory used; 8.9 GB of 13.6 GB virtual memory used. Killing container. > >> > Dump of the process-tree for container_1418082773407_8587_02_000001 : > >> > |- PID PPID PGRPID SESSID CMD_NAME USER_MODE_TIME(MILLIS) > >> > SYSTEM_TIME(MILLIS) VMEM_USAGE(BYTES) RSSMEM_USAGE(PAGES) > FULL_CMD_LINE > >> > |- 19790 19788 19790 19790 (bash) 0 0 110809088 336 /bin/bash -c > >> > /usr/java/jdk1.7.0_67-cloudera/bin/java -server -Xmx6144m > >> > > >> > > -Djava.io.tmpdir=/mnt/DATA1/yarn/nm/usercache/kakn/appcache/application_1418082773407_8587/container_1418082773407_8587_02_000001/tmp > >> > '-Dspark.executor.memory=8g' '-Dspark.eventLog.enabled=true' > >> > '-Dspark.yarn.secondary.jars=' > >> > '-Dspark.app.name=ConnectedComponentsTest' > >> > > >> > > '-Dspark.eventLog.dir=hdfs://<server-name-replaced>:8020/user/spark/applicationHistory' > >> > '-Dspark.master=yarn-cluster' > >> > org.apache.spark.deploy.yarn.ApplicationMaster > >> > --class 'ConnectedComponentsTest' --jar > >> > > >> > > 'file:/home/kakn01/Spark/SparkSource/target/scala-2.10/connectedcomponentstest_2.10-1.0.jar' > >> > --executor-memory 8192 --executor-cores 1 --num-executors 7 1> > >> > > >> > > /var/log/hadoop-yarn/container/application_1418082773407_8587/container_1418082773407_8587_02_000001/stdout > >> > 2> > >> > > >> > > /var/log/hadoop-yarn/container/application_1418082773407_8587/container_1418082773407_8587_02_000001/stderr > >> > |- 19794 19790 19790 19790 (java) 205066 9152 9477726208 1707599 > >> > /usr/java/jdk1.7.0_67-cloudera/bin/java -server -Xmx6144m > >> > > >> > > -Djava.io.tmpdir=/mnt/DATA1/yarn/nm/usercache/kakn/appcache/application_1418082773407_8587/container_1418082773407_8587_02_000001/tmp > >> > -Dspark.executor.memory=8g -Dspark.eventLog.enabled=true > >> > -Dspark.yarn.secondary.jars= -Dspark.app.name=ConnectedComponentsTest > >> > > >> > > -Dspark.eventLog.dir=hdfs://<server-name-replaced>:8020/user/spark/applicationHistory > >> > -Dspark.master=yarn-cluster > >> > org.apache.spark.deploy.yarn.ApplicationMaster > >> > --class ConnectedComponentsTest --jar > >> > > >> > > file:/home/kakn01/Spark/SparkSource/target/scala-2.10/connectedcomponentstest_2.10-1.0.jar > >> > --executor-memory 8192 --executor-cores 1 --num-executors 7 > >> > Container killed on request. Exit code is 143 > >> > Container exited with a non-zero exit code 143 > >> > .Failing this attempt.. Failing the application. > >> > > >> > > >> > > >> > -- > >> > View this message in context: > >> > > http://apache-spark-user-list.1001560.n3.nabble.com/Running-beyond-memory-limits-in-ConnectedComponents-tp21139.html > >> > Sent from the Apache Spark User List mailing list archive at > Nabble.com. > >> > > >> > --------------------------------------------------------------------- > >> > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > >> > For additional commands, e-mail: user-h...@spark.apache.org > >> > > > > > >
