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https://issues.apache.org/jira/browse/SPARK-22622?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16269974#comment-16269974
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Raghavendra edited comment on SPARK-22622 at 11/29/17 2:36 AM:
---------------------------------------------------------------
[~srowen] I am broadcasting all my large data strucures and im assuming that
the actual RDD is not serialized via the closure serializer. Then what is
causing this error? Even if there is a specific data causing this issue,a
proactive check by the Spark Serializer reporting the exact culprit would be
very helpful.
Also, how about my second issue. Even though the issue could be large data, the
Job must end gracefully. The Log4j Logs are receiving this stack trace but the
Spark UI is not showing anything and the Job is not ending on this failure,
instead the "dag-event-loop" thread is killed and the jobs continues
indefinitely. I think the latter is a bug that needs to be handled at least.
Kindly clarify.
was (Author: ragzisme):
[~srowen] I am broadcasting all my large data strucures and im assuming that
the actual RDD is not serialized via the closure serializer. Then what is
causing this error?
Also, how about my second issue. Even though the issue could be large data, the
Job must end gracefully. The Log4j Logs are receiving this stack trace but the
Spark UI is not showing anything and the Job is not ending on this failure,
instead the "dag-event-loop" thread is killed and the jobs continues
indefinitely. I think the latter is a bug that needs to be handled at least.
Kindly clarify.
> OutOfMemory thrown by Closure Serializer without proper failure propagation
> ---------------------------------------------------------------------------
>
> Key: SPARK-22622
> URL: https://issues.apache.org/jira/browse/SPARK-22622
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.2.0
> Environment: Spark 2.2.0
> Hadoop 2.9.0
> Reporter: Raghavendra
> Priority: Critical
>
> While moving from a Stage to another, the Closure serializer is trying to
> Serialize the Closures and throwing OOMs.
> This is happening when the RDD size crosses 70 GB.
> I set the Driver Memory to 225 GB and yet the error persist.
> There are two issues here
> * OOM thrown when there is almost 3 times of Driver memory provided than the
> last Stage RDD size.(Even tried caching this into the disk before moving it
> into the current stage)
> * After the Error is thrown, the Spark Job does not exit. it just continues
> in the same state without propagating the error into the Spark UI.
> *Scenario 1*
> {color:red}Exception in thread "dag-scheduler-event-loop"
> java.lang.OutOfMemoryError: Requested array size exceeds VM limit
> at java.util.Arrays.copyOf(Arrays.java:3236)
> at java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:118)
> at
> java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93)
> at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:153)
> at
> org.apache.spark.util.ByteBufferOutputStream.write(ByteBufferOutputStream.scala:41)
> at
> java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877)
> at
> java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1786)
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1189)
> at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
> at
> org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:43)
> at
> org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:100)
> at
> org.apache.spark.scheduler.DAGScheduler.submitMissingTasks(DAGScheduler.scala:1003)
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:930)
> at
> org.apache.spark.scheduler.DAGScheduler.handleJobSubmitted(DAGScheduler.scala:874)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1677)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> {color}
> *Scenario 2*
> {color:red}
> Exception in thread "dag-scheduler-event-loop"
> java.lang.OutOfMemoryError
> at
> java.io.ByteArrayOutputStream.hugeCapacity(ByteArrayOutputStream.java:123)
> at java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:117)
> at
> java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93)
> at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:153)
> at
> org.apache.spark.util.ByteBufferOutputStream.write(ByteBufferOutputStream.scala:41)
> at
> java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877)
> at
> java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1786)
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1189)
> at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
> at
> org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:43)
> at
> org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:100)
> at
> org.apache.spark.scheduler.DAGScheduler.submitMissingTasks(DAGScheduler.scala:1003)
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:930)
> at
> org.apache.spark.scheduler.DAGScheduler.handleJobSubmitted(DAGScheduler.scala:874)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1677)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> {color}
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