Spark Listener EventLoggingListener引发了异常/ ConcurrentModificationException

Question

在我们的应用程序(Spark 2.0.1)中,我们经常出现此异常.我找不到任何关于此事的内容.可能是什么原因 ？

16/10/27 11:18:24 ERROR LiveListenerBus: Listener EventLoggingListener threw an exception
java.util.ConcurrentModificationException
    at java.util.ArrayList$Itr.checkForComodification(ArrayList.java:901)
    at java.util.ArrayList$Itr.next(ArrayList.java:851)
    at scala.collection.convert.Wrappers$JIteratorWrapper.next(Wrappers.scala:43)
    at scala.collection.Iterator$class.foreach(Iterator.scala:893)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
    at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
    at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
    at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
    at scala.collection.mutable.ListBuffer.$plus$plus$eq(ListBuffer.scala:183)
    at scala.collection.mutable.ListBuffer.$plus$plus$eq(ListBuffer.scala:45)
    at scala.collection.TraversableLike$class.to(TraversableLike.scala:590)
    at scala.collection.AbstractTraversable.to(Traversable.scala:104)
    at scala.collection.TraversableOnce$class.toList(TraversableOnce.scala:294)
    at scala.collection.AbstractTraversable.toList(Traversable.scala:104)
    at org.apache.spark.util.JsonProtocol$.accumValueToJson(JsonProtocol.scala:314)
    at org.apache.spark.util.JsonProtocol$$anonfun$accumulableInfoToJson$5.apply(JsonProtocol.scala:291)
    at org.apache.spark.util.JsonProtocol$$anonfun$accumulableInfoToJson$5.apply(JsonProtocol.scala:291)
    at scala.Option.map(Option.scala:146)
    at org.apache.spark.util.JsonProtocol$.accumulableInfoToJson(JsonProtocol.scala:291)
    at org.apache.spark.util.JsonProtocol$$anonfun$taskInfoToJson$12.apply(JsonProtocol.scala:283)
    at org.apache.spark.util.JsonProtocol$$anonfun$taskInfoToJson$12.apply(JsonProtocol.scala:283)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at scala.collection.generic.TraversableForwarder$class.foreach(TraversableForwarder.scala:35)
    at scala.collection.mutable.ListBuffer.foreach(ListBuffer.scala:45)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
    at scala.collection.AbstractTraversable.map(Traversable.scala:104)
    at org.apache.spark.util.JsonProtocol$.taskInfoToJson(JsonProtocol.scala:283)
    at org.apache.spark.util.JsonProtocol$.taskEndToJson(JsonProtocol.scala:145)
    at org.apache.spark.util.JsonProtocol$.sparkEventToJson(JsonProtocol.scala:76)
    at org.apache.spark.scheduler.EventLoggingListener.logEvent(EventLoggingListener.scala:137)
    at org.apache.spark.scheduler.EventLoggingListener.onTaskEnd(EventLoggingListener.scala:157)
    at org.apache.spark.scheduler.SparkListenerBus$class.doPostEvent(SparkListenerBus.scala:45)
    at org.apache.spark.scheduler.LiveListenerBus.doPostEvent(LiveListenerBus.scala:36)
    at org.apache.spark.scheduler.LiveListenerBus.doPostEvent(LiveListenerBus.scala:36)
    at org.apache.spark.util.ListenerBus$class.postToAll(ListenerBus.scala:63)
    at org.apache.spark.scheduler.LiveListenerBus.postToAll(LiveListenerBus.scala:36)
    at org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(LiveListenerBus.scala:94)
    at org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(LiveListenerBus.scala:79)
    at org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(LiveListenerBus.scala:79)
    at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
    at org.apache.spark.scheduler.LiveListenerBus$$anon$1$$anonfun$run$1.apply$mcV$sp(LiveListenerBus.scala:78)
    at org.apache.spark.util.Utils$.tryOrStopSparkContext(Utils.scala:1249)
    at org.apache.spark.scheduler.LiveListenerBus$$anon$1.run(LiveListenerBus.scala:77)

编辑:还有一个信息,我们的应用程序是长期运行的,并且从可能失败的spark上下文中恢复,我们在两个"作业"之间使用SparkBuilder.getOrCreate()方法.这会让听众搞砸吗？

Answer 1

这是Spark 2.0.1(SPARK-17816)中的一个已知问题,将通过Spark 2.0.2/2.1.0(相关拉取请求)修复.

要在不等待的情况下摆脱异常Spark 2.0.2/2.1.0,请克隆最新的,不稳定的spark版本并手动构建apache-spark.

更新:他们发布了Spark 2.0.2!