解决spark运行时Java heap space问题

问题描述：

在执行spark程序时，需要读取200w数据作为缓存。在利用.broadcast广播这些数据时，遇到Exception in thread "main" java.lang.OutOfMemoryError: Java heap space问题。

报错信息如下：

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$.blockifyObject(TorrentBroadcast.scala:202)
        at org.apache.spark.broadcast.TorrentBroadcast.writeBlocks(TorrentBroadcast.scala:101)
        at org.apache.spark.broadcast.TorrentBroadcast.(TorrentBroadcast.scala:84)
        at org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast(TorrentBroadcastFactory.scala:34)
        at org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast(TorrentBroadcastFactory.scala:29)
        at org.apache.spark.broadcast.BroadcastManager.newBroadcast(BroadcastManager.scala:62)
        at org.apache.spark.SparkContext.broadcast(SparkContext.scala:1051)
        at org.apache.spark.api.java.JavaSparkContext.broadcast(JavaSparkContext.scala:648)
        at com.myspark.spark.task.Spark_task.main(Spark_task.java:77)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:497)
        at org.apache.spark.deploy.SparkSubmit$.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)

进一步地，查看报错位置之前的几句信息：

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

说明内存不够了。

解决办法：

spark不能通过java -Xms32m -Xmx800m className来添加内存，spark不支持该格式，从./bin/spark-submit --help中也没有看到该格式。所以只能从spark本身入手。

查看./bin/spark-submit --help，发现

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

于是，修改运行提交语句为，运行成功：

./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

对于executor-memory，由于我是在基于yarn的spark上运行的，可能这个是有yarn自己来控制。这里设置时，是无效的。可能在local模式时，可以设置。具体细节待实验研究。

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

【附】

./bin/spark-submit --help具体信息如下：

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.