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https://issues.apache.org/jira/browse/SPARK-17430?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15551906#comment-15551906
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dvir commented on SPARK-17430:
------------------------------
what's up with that? we have the same problem. spark just hangs forever.
> Spark task Hangs after OOM while DAG scheduler tries to serialize a task
> ------------------------------------------------------------------------
>
> Key: SPARK-17430
> URL: https://issues.apache.org/jira/browse/SPARK-17430
> Project: Spark
> Issue Type: Bug
> Components: Scheduler
> Affects Versions: 1.6.2
> Reporter: Mikhail Galanin
> Attachments: abort-task-on-oom-in-dag-scheduler.patch
>
>
> Hi here,
> We're running Spark under Hadoop 2.7.1 Yarn and faced a problem.
> The problem is that sometimes an exception raises inside JavaSerializer (see
> the stacktrace below). The exception isn't a problem itself but after it
> happens, the task hangs. It's shown as "running" in the Hadoop task list but
> no one worker is executing task, no more records appear in Spark job log
> until somebody kills it.
> We have fixed the issue by patching Spark code (catch OOM in
> submitMissingTasks()) but it looks like OOM error is deliberately ignored so
> probably there should be a better solution.
> {noformat}
> Exception in thread "dag-scheduler-event-loop" java.lang.OutOfMemoryError:
> Java heap space
> at java.util.Arrays.copyOf(Arrays.java:3332)
> at
> java.lang.AbstractStringBuilder.expandCapacity(AbstractStringBuilder.java:137)
> at
> java.lang.AbstractStringBuilder.ensureCapacityInternal(AbstractStringBuilder.java:121)
> at
> java.lang.AbstractStringBuilder.append(AbstractStringBuilder.java:421)
> at java.lang.StringBuilder.append(StringBuilder.java:136)
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1421)
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
> at
> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
> at
> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
> at
> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
> at
> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
> at
> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
> at
> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
> at
> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
> at
> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
> 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.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:101)
> at
> org.apache.spark.scheduler.DAGScheduler.submitMissingTasks(DAGScheduler.scala:1003)
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:921)
> at
> org.apache.spark.scheduler.DAGScheduler.handleJobSubmitted(DAGScheduler.scala:861)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1607)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
> {noformat}
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