我在HDP 2.3.2.0集群上使用Spark 1.4.1，我有一个简单的应用程序来创建从Kafka读取数据并对其应用过滤器转换的dstream。此应用程序使用主纱线客户端启动。或多或少一天后会引发以下异常：
Exception in thread "dag-scheduler-event-loop" java.lang.OutOfMemoryError: Java heap space
at org.apache.spark.util.io.ByteArrayChunkOutputStream.allocateNewChunkIfNeeded(ByteArrayChunkOutputStream.scala:66)
at org.apache.spark.util.io.ByteArrayChunkOutputStream.write(ByteArrayChunkOutputStream.scala:55)
at org.xerial.snappy.SnappyOutputStream.dumpOutput(SnappyOutputStream.java:294)
at org.xerial.snappy.SnappyOutputStream.flush(SnappyOutputStream.java:273)
at org.xerial.snappy.SnappyOutputStream.close(SnappyOutputStream.java:324)
at org.apache.spark.io.SnappyOutputStreamWrapper.close(CompressionCodec.scala:203)
at com.esotericsoftware.kryo.io.Output.close(Output.java:168)
at org.apache.spark.serializer.KryoSerializationStream.close(KryoSerializer.scala:162)
at org.apache.spark.broadcast.TorrentBroadcast$.blockifyObject(TorrentBroadcast.scala:203)
at org.apache.spark.broadcast.TorrentBroadcast.writeBlocks(TorrentBroadcast.scala:102)
at org.apache.spark.broadcast.TorrentBroadcast.<init>(TorrentBroadcast.scala:85)
at org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast(TorrentBroadcastFactory.scala:34)
at org.apache.spark.broadcast.BroadcastManager.newBroadcast(BroadcastManager.scala:62)
at org.apache.spark.SparkContext.broadcast(SparkContext.scala:1291)
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitMissingTasks(DAGScheduler.scala:874)
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:815)
at org.apache.spark.scheduler.DAGScheduler.handleJobSubmitted(DAGScheduler.scala:799)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1426)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1418)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
[Stage 53513:> (0 + 0) / 4]Exception in thread "JobGenerator" java.lang.OutOfMemoryError: GC overhead limit exceeded
at sun.net.www.protocol.jar.Handler.openConnection(Handler.java:41)
at java.net.URL.openConnection(URL.java:972)
at java.net.URLClassLoader.getResourceAsStream(URLClassLoader.java:237)
at java.lang.Class.getResourceAsStream(Class.java:2223)
at org.apache.spark.util.ClosureCleaner$.getClassReader(ClosureCleaner.scala:38)
at org.apache.spark.util.ClosureCleaner$.getInnerClosureClasses(ClosureCleaner.scala:98)
at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:197)
at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:132)
at org.apache.spark.SparkContext.clean(SparkContext.scala:1893)
at org.apache.spark.rdd.RDD$$anonfun$map$1.apply(RDD.scala:294)
at org.apache.spark.rdd.RDD$$anonfun$map$1.apply(RDD.scala:293)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:108)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:286)
at org.apache.spark.rdd.RDD.map(RDD.scala:293)
at org.apache.spark.streaming.dstream.MappedDStream$$anonfun$compute$1.apply(MappedDStream.scala:35)
at org.apache.spark.streaming.dstream.MappedDStream$$anonfun$compute$1.apply(MappedDStream.scala:35)
at scala.Option.map(Option.scala:145)
at org.apache.spark.streaming.dstream.MappedDStream.compute(MappedDStream.scala:35)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:350)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:350)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:349)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:349)
at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:399)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:344)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:342)
at scala.Option.orElse(Option.scala:257)
at org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:339)
at org.apache.spark.streaming.dstream.FilteredDStream.compute(FilteredDStream.scala:35)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:350)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:350)
我转储了驱动程序进程的堆，似乎 org.apache.spark.deploy.yarn.history.YarnHistoryService 类的实例使用了 512 MB 可用内存中的 486.2 MB 包含大量类 org.apache.spark.deploy.history.yarn.HandleSparkEvent 的实例。我试图弄清楚如何解决问题，但直到现在我还没有找到解决方案。
有人可以帮助我解决问题吗？
谢谢
在通过Spark用户列表（http://apache-spark-user-list.1001560.n3.nabble.com/）与Hortonworks工程师交换电子邮件后，我解决了这个问题。基本上有一个错误，它不允许驱动程序将对象org.apache.spark.deploy.history.yarn.HandleSparkEvent发布到YARN时间线服务器，因此随着时间的推移，这些消息的数量越来越大，耗尽分配给驱动程序的内存。为了避免这个问题，我从文件spark-default.conf中删除了spark.yarn.services条目。