問題描述：

在執行spark程式時，需要讀取200w資料作為快取。在利用.broadcast廣播這些資料時，遇到Exception in thread "main" java.lang.OutOfMemoryError: Java heap space問題。

報錯資訊如下：

15/09/15 05:26:09 INFO storage.BlockManagerInfo: Removed broadcast_3_piece0 on ip-172-31-10-136.ec2.internal:34472 in memory (size: 2.0 KB, free: 397.3 MB)
15/09/15 05:26:09 INFO spark.ContextCleaner: Cleaned broadcast 3
Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
        at java.io.ObjectOutputStream$HandleTable.growEntries(ObjectOutputStream.java:2351)
        at java.io.ObjectOutputStream$HandleTable.assign(ObjectOutputStream.java:2276)
        at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1428)
        at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
        at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
        at java.util.ArrayList.writeObject(ArrayList.java:762)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:497)
        at java.io.ObjectStreamClass.invokeWriteObject(ObjectStreamClass.java:988)
        at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1496)
        at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
        at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
        at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
        at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:44)
        at org.apache.spark.broadcast.TorrentBroadcast$.blockifyObject(TorrentBroadcast.scala:202)
        at org.apache.spark.broadcast.TorrentBroadcast.writeBlocks(TorrentBroadcast.scala:101)
        at org.apache.spark.broadcast.TorrentBroadcast.<init>(TorrentBroadcast.scala:84)
        at org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast(TorrentBroadcastFactory.scala:34)
        at org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast(TorrentBroadcastFactory.scala:29)
        at org.apache.spark.broadcast.BroadcastManager.newBroadcast(BroadcastManager.scala:62)
        at org.apache.spark.SparkContext.broadcast(SparkContext.scala:1051)
        at org.apache.spark.api.java.JavaSparkContext.broadcast(JavaSparkContext.scala:648)
        at com.myspark.spark.task.Spark_task.main(Spark_task.java:77)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:497)
        at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:569)
        at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:166)
        at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:189)
 

15/09/15 05:26:09 INFO storage.MemoryStore: Block broadcast_3 of size 3488 dropped from memory (free 280236528)
15/09/15 05:26:09 INFO storage.BlockManagerInfo: Removed broadcast_3_piece0 on ip-172-31-10-135.ec2.internal:51942 in memory (size: 2.0 KB, free: 398.1 MB)
15/09/15 05:26:09 INFO storage.BlockManagerInfo: Removed broadcast_3_piece0 on ip-172-31-10-136.ec2.internal:34472 in memory (size: 2.0 KB, free: 397.3 MB)
15/09/15 05:26:09 INFO spark.ContextCleaner: Cleaned broadcast 3
 

解決辦法：

spark不能通過java -Xms32m -Xmx800m className來新增記憶體，spark不支援該格式，從./bin/spark-submit --help中也沒有看到該格式。所以只能從spark本身入手。

檢視./bin/spark-submit --help，發現

 --driver-memory MEM         Memory for driver (e.g. 1000M, 2G) (Default: 512M).

./bin/spark-submit 
 --class com.myspark.spark.task.Spark_task 
 --master yarn-client --driver-memory 1g
 /home/hadoop/myspark/spark-example-test-0.0.1-SNAPSHOT.jar
 s3://**********
 s3://***********
 /test/myspark/spark35
 

對於executor-memory，由於我是在基於yarn的spark上執行的，可能這個是有yarn自己來控制。這裡設定時，是無效的。可能在local模式時，可以設定。具體細節待實驗研究。

--executor-memory MEM       Memory per executor (e.g. 1000M, 2G) (Default: 1G)

./bin/spark-submit --help具體資訊如下：

Options:
  --master MASTER_URL         spark://host:port, mesos://host:port, yarn, or local.
  --deploy-mode DEPLOY_MODE   Whether to launch the driver program locally ("client") or
                              on one of the worker machines inside the cluster ("cluster")
                              (Default: client).
  --class CLASS_NAME          Your application's main class (for Java / Scala apps).
  --name NAME                 A name of your application.
  --jars JARS                 Comma-separated list of local jars to include on the driver
                              and executor classpaths.
  --packages                  Comma-separated list of maven coordinates of jars to include
                              on the driver and executor classpaths. Will search the local
                              maven repo, then maven central and any additional remote
                              repositories given by --repositories. The format for the
                              coordinates should be groupId:artifactId:version.
  --repositories              Comma-separated list of additional remote repositories to
                              search for the maven coordinates given with --packages.
  --py-files PY_FILES         Comma-separated list of .zip, .egg, or .py files to place
                              on the PYTHONPATH for Python apps.
  --files FILES               Comma-separated list of files to be placed in the working
                              directory of each executor.

  --conf PROP=VALUE           Arbitrary Spark configuration property.
  --properties-file FILE      Path to a file from which to load extra properties. If not
                              specified, this will look for conf/spark-defaults.conf.

  --driver-memory MEM         Memory for driver (e.g. 1000M, 2G) (Default: 512M).
  --driver-java-options       Extra Java options to pass to the driver.
  --driver-library-path       Extra library path entries to pass to the driver.
  --driver-class-path         Extra class path entries to pass to the driver. Note that
                              jars added with --jars are automatically included in the
                              classpath.

  --executor-memory MEM       Memory per executor (e.g. 1000M, 2G) (Default: 1G).

  --proxy-user NAME           User to impersonate when submitting the application.

  --help, -h                  Show this help message and exit
  --verbose, -v               Print additional debug output
  --version,                  Print the version of current Spark

 Spark standalone with cluster deploy mode only:
  --driver-cores NUM          Cores for driver (Default: 1).
  --supervise                 If given, restarts the driver on failure.
  --kill SUBMISSION_ID        If given, kills the driver specified.
  --status SUBMISSION_ID      If given, requests the status of the driver specified.

 Spark standalone and Mesos only:
  --total-executor-cores NUM  Total cores for all executors.

 YARN-only:
  --driver-cores NUM          Number of cores used by the driver, only in cluster mode
                              (Default: 1).
  --executor-cores NUM        Number of cores per executor (Default: 1).
  --queue QUEUE_NAME          The YARN queue to submit to (Default: "default").
  --num-executors NUM         Number of executors to launch (Default: 2).
  --archives ARCHIVES         Comma separated list of archives to be extracted into the
                              working directory of each executor.