hi i'am testing SimpleApp.scala in standalone mode with only one pc, so i have one master and one local worker on the same pc
with rather small input file size(4.5K), i have got the java.lang.OutOfMemoryError: Java heap space error here's my settings: spark-env.sh: export SPARK_MASTER_IP="127.0.0.1" export SPARK_WORKER_CORES=1 export SPARK_WORKER_MEMORY=2g export SPARK_JAVA_OPTS+=" -Xms512m -Xmx512m " //(1) SimpleApp.scala: val conf = new SparkConf() .setMaster("spark://127.0.0.1:7077") .setAppName("Simple App") .set("spark.executor.memory", "1g") //(2) val sc = new SparkContext(conf) sbt: SBT_OPTS="-Xms512M -Xmx512M" //(3) java $SBT_OPTS -jar `dirname $0`/sbt-launch.jar "$@" i'am confused with the above (1)(2)(3) settings, and tried several different options, but all failed with java.lang.OutOfMemoryError:( what's the difference between JVM heap size and spark.executor.memory and how to set them? i've read some docs and still cannot fully understand spark.executor.memory: Amount of memory to use per executor process, in the same format as JVM memory strings (e.g. 512m, 2g). spark.storage.memoryFraction: Fraction of Java heap to use for Spark's memory cache. spark.storage.memoryFraction = 0.6 * spark.executor.memory is that mean spark.executor.memory = JVM heap size? here's the logs: [info] Running SimpleApp 14/04/24 10:59:41 WARN util.Utils: Your hostname, ubuntu resolves to a loopback address: 127.0.1.1; using 192.168.0.113 instead (on interface eth0) 14/04/24 10:59:41 WARN util.Utils: Set SPARK_LOCAL_IP if you need to bind to another address 14/04/24 10:59:42 INFO slf4j.Slf4jLogger: Slf4jLogger started 14/04/24 10:59:42 INFO Remoting: Starting remoting 14/04/24 10:59:42 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://spark@ubuntu.local:46864] 14/04/24 10:59:42 INFO Remoting: Remoting now listens on addresses: [akka.tcp://spark@ubuntu.local:46864] 14/04/24 10:59:42 INFO spark.SparkEnv: Registering BlockManagerMaster 14/04/24 10:59:42 INFO storage.DiskBlockManager: Created local directory at /tmp/spark-local-20140424105942-362c 14/04/24 10:59:42 INFO storage.MemoryStore: MemoryStore started with capacity 297.0 MB. 14/04/24 10:59:42 INFO network.ConnectionManager: Bound socket to port 34146 with id = ConnectionManagerId(ubuntu.local,34146) 14/04/24 10:59:42 INFO storage.BlockManagerMaster: Trying to register BlockManager 14/04/24 10:59:42 INFO storage.BlockManagerMasterActor$BlockManagerInfo: Registering block manager ubuntu.local:34146 with 297.0 MB RAM 14/04/24 10:59:42 INFO storage.BlockManagerMaster: Registered BlockManager 14/04/24 10:59:43 INFO spark.HttpServer: Starting HTTP Server 14/04/24 10:59:43 INFO server.Server: jetty-7.6.8.v20121106 14/04/24 10:59:43 INFO server.AbstractConnector: Started SocketConnector@0.0.0.0:58936 14/04/24 10:59:43 INFO broadcast.HttpBroadcast: Broadcast server started at http://192.168.0.113:58936 14/04/24 10:59:43 INFO spark.SparkEnv: Registering MapOutputTracker 14/04/24 10:59:43 INFO spark.HttpFileServer: HTTP File server directory is /tmp/spark-ce78fc2c-097d-4053-991d-b6bf140d6c33 14/04/24 10:59:43 INFO spark.HttpServer: Starting HTTP Server 14/04/24 10:59:43 INFO server.Server: jetty-7.6.8.v20121106 14/04/24 10:59:43 INFO server.AbstractConnector: Started SocketConnector@0.0.0.0:56414 14/04/24 10:59:43 INFO server.Server: jetty-7.6.8.v20121106 14/04/24 10:59:43 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/storage/rdd,null} 14/04/24 10:59:43 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/storage,null} 14/04/24 10:59:43 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/stages/stage,null} 14/04/24 10:59:43 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/stages/pool,null} 14/04/24 10:59:43 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/stages,null} 14/04/24 10:59:43 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/environment,null} 14/04/24 10:59:43 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/executors,null} 14/04/24 10:59:43 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/metrics/json,null} 14/04/24 10:59:43 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/static,null} 14/04/24 10:59:43 INFO handler.ContextHandler: started o.e.j.s.h.ContextHandler{/,null} 14/04/24 10:59:43 INFO server.AbstractConnector: Started SelectChannelConnector@0.0.0.0:4040 14/04/24 10:59:43 INFO ui.SparkUI: Started Spark Web UI at http://ubuntu.local:4040 14/04/24 10:59:43 INFO client.AppClient$ClientActor: Connecting to master spark://127.0.0.1:7077... 14/04/24 10:59:44 INFO cluster.SparkDeploySchedulerBackend: Connected to Spark cluster with app ID app-20140424105944-0001 14/04/24 10:59:44 INFO client.AppClient$ClientActor: Executor added: app-20140424105944-0001/0 on worker-20140424105022-ubuntu.local-40058 (ubuntu.local:40058) with 1 cores 14/04/24 10:59:44 INFO cluster.SparkDeploySchedulerBackend: Granted executor ID app-20140424105944-0001/0 on hostPort ubuntu.local:40058 with 1 cores, 1024.0 MB RAM 14/04/24 10:59:44 INFO client.AppClient$ClientActor: Executor updated: app-20140424105944-0001/0 is now RUNNING 14/04/24 10:59:45 INFO storage.MemoryStore: ensureFreeSpace(32960) called with curMem=0, maxMem=311387750 14/04/24 10:59:45 INFO storage.MemoryStore: Block broadcast_0 stored as values to memory (estimated size 32.2 KB, free 296.9 MB) 14/04/24 10:59:45 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 14/04/24 10:59:45 WARN snappy.LoadSnappy: Snappy native library not loaded 14/04/24 10:59:45 INFO mapred.FileInputFormat: Total input paths to process : 1 14/04/24 10:59:45 INFO spark.SparkContext: Starting job: count at SimpleApp.scala:27 14/04/24 10:59:45 INFO scheduler.DAGScheduler: Got job 0 (count at SimpleApp.scala:27) with 2 output partitions (allowLocal=false) 14/04/24 10:59:45 INFO scheduler.DAGScheduler: Final stage: Stage 0 (count at SimpleApp.scala:27) 14/04/24 10:59:45 INFO scheduler.DAGScheduler: Parents of final stage: List() 14/04/24 10:59:45 INFO scheduler.DAGScheduler: Missing parents: List() 14/04/24 10:59:45 INFO scheduler.DAGScheduler: Submitting Stage 0 (MappedRDD[1] at textFile at SimpleApp.scala:25), which has no missing parents 14/04/24 10:59:45 INFO scheduler.DAGScheduler: Submitting 2 missing tasks from Stage 0 (MappedRDD[1] at textFile at SimpleApp.scala:25) 14/04/24 10:59:45 INFO scheduler.TaskSchedulerImpl: Adding task set 0.0 with 2 tasks 14/04/24 10:59:46 INFO cluster.SparkDeploySchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@ubuntu.local:41819/user/Executor#84992753] with ID 0 14/04/24 10:59:47 INFO scheduler.TaskSetManager: Starting task 0.0:0 as TID 0 on executor 0: ubuntu.local (PROCESS_LOCAL) 14/04/24 10:59:47 INFO scheduler.TaskSetManager: Serialized task 0.0:0 as 1563 bytes in 17 ms 14/04/24 10:59:47 INFO storage.BlockManagerMasterActor$BlockManagerInfo: Registering block manager ubuntu.local:60938 with 593.9 MB RAM 14/04/24 10:59:49 INFO scheduler.TaskSetManager: Starting task 0.0:1 as TID 1 on executor 0: ubuntu.local (PROCESS_LOCAL) 14/04/24 10:59:49 INFO scheduler.TaskSetManager: Serialized task 0.0:1 as 1563 bytes in 0 ms 14/04/24 10:59:49 WARN scheduler.TaskSetManager: Lost TID 0 (task 0.0:0) 14/04/24 10:59:49 WARN scheduler.TaskSetManager: Loss was due to java.lang.OutOfMemoryError java.lang.OutOfMemoryError: Java heap space at org.apache.hadoop.io.WritableUtils.readCompressedStringArray(WritableUtils.java:183) at org.apache.hadoop.conf.Configuration.readFields(Configuration.java:2378) at org.apache.hadoop.io.ObjectWritable.readObject(ObjectWritable.java:285) at org.apache.hadoop.io.ObjectWritable.readFields(ObjectWritable.java:77) at org.apache.spark.SerializableWritable.readObject(SerializableWritable.scala:39) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:606) at java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1017) at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1891) at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1796) at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1348) at java.io.ObjectInputStream.readObject(ObjectInputStream.java:370) at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:40) at org.apache.spark.broadcast.HttpBroadcast$.read(HttpBroadcast.scala:165) at org.apache.spark.broadcast.HttpBroadcast.readObject(HttpBroadcast.scala:56) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:606) at java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1017) at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1891) at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1796) at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1348) at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1989) at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1913) at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1796) at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1348) at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1989) at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1913) at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1796) appreciate your help -- View this message in context: 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