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https://issues.apache.org/jira/browse/SPARK-22622?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Raghavendra reopened SPARK-22622:
---------------------------------
The output of previous Stage is around 70GB and my driver memory is 225GB. Why
am i getting an OOM when there is sufficient memory.
Also, since this is closure serializer, isnt it only serializing the Physical
Plan and not the RDD?
17/11/28 07:22:53 INFO DAGScheduler: Submitting ResultStage 3
(MapPartitionsRDD[25] at parquet at LogProcessor.java:473), which has no
missing parents
17/11/28 07:22:53 ERROR LogListener: RDD Info : MapPartitionsRDD - 25 - 0 - 0 -
StorageLevel(1 replicas) - parquet at LogProcessor.java:473
17/11/28 07:22:53 ERROR LogListener: RDD Info : MapPartitionsRDD - 20 - 0 - 0 -
StorageLevel(1 replicas) - persist at LogProcessor.java:283
17/11/28 07:22:53 ERROR LogListener: RDD Info : MapPartitionsRDD - 19 - 0 - 0 -
StorageLevel(1 replicas) - persist at LogProcessor.java:283
17/11/28 07:22:53 ERROR LogListener: RDD Info : MapPartitionsRDD - 12 - 0 - 0 -
StorageLevel(1 replicas) - persist at LogProcessor.java:88
17/11/28 07:22:53 ERROR LogListener: RDD Info : FileScanRDD - 11 - 0 - 0 -
StorageLevel(1 replicas) - persist at LogProcessor.java:88
17/11/28 07:22:53 ERROR LogListener: RDD Info : MapPartitionsRDD - 22 - 0 - 0 -
StorageLevel(1 replicas) - persist at LogProcessor.java:283
17/11/28 07:22:53 ERROR LogListener: RDD Info : MapPartitionsRDD - 18 - 0 - 0 -
StorageLevel(1 replicas) - persist at LogProcessor.java:283
17/11/28 07:22:53 ERROR LogListener: RDD Info : MapPartitionsRDD - 21 - 0 - 0 -
StorageLevel(1 replicas) - persist at LogProcessor.java:283
17/11/28 07:22:53 ERROR LogListener: RDD Info : MapPartitionsRDD - 24 - 0 - 0 -
StorageLevel(1 replicas) - parquet at LogProcessor.java:473
> OutOfMemory thrown by Closure Serializer
> ----------------------------------------
>
> 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 persists.
> {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}
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