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From "Colin Ma (JIRA)" <j...@apache.org>
Subject [jira] [Created] (HIVE-16004) OutOfMemory in SparkReduceRecordHandler with vectorization mode
Date Wed, 22 Feb 2017 06:30:45 GMT
Colin Ma created HIVE-16004:
-------------------------------

             Summary: OutOfMemory in SparkReduceRecordHandler with vectorization mode
                 Key: HIVE-16004
                 URL: https://issues.apache.org/jira/browse/HIVE-16004
             Project: Hive
          Issue Type: Bug
            Reporter: Colin Ma
            Assignee: Colin Ma


For the query 28 of TPCs-BB with 1T data, the executor memory is set as 30G. Get the following
exception:
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 java.io.DataOutputStream.write(DataOutputStream.java:107)
	at org.apache.hadoop.hive.ql.exec.vector.VectorizedBatchUtil.setVector(VectorizedBatchUtil.java:467)
	at org.apache.hadoop.hive.ql.exec.vector.VectorizedBatchUtil.addRowToBatchFrom(VectorizedBatchUtil.java:238)
	at org.apache.hadoop.hive.ql.exec.spark.SparkReduceRecordHandler.processVectors(SparkReduceRecordHandler.java:367)
	at org.apache.hadoop.hive.ql.exec.spark.SparkReduceRecordHandler.processRow(SparkReduceRecordHandler.java:286)
	at org.apache.hadoop.hive.ql.exec.spark.SparkReduceRecordHandler.processRow(SparkReduceRecordHandler.java:220)
	at org.apache.hadoop.hive.ql.exec.spark.HiveReduceFunctionResultList.processNextRecord(HiveReduceFunctionResultList.java:49)
	at org.apache.hadoop.hive.ql.exec.spark.HiveReduceFunctionResultList.processNextRecord(HiveReduceFunctionResultList.java:28)
	at org.apache.hadoop.hive.ql.exec.spark.HiveBaseFunctionResultList.hasNext(HiveBaseFunctionResultList.java:85)
	at scala.collection.convert.Wrappers$JIteratorWrapper.hasNext(Wrappers.scala:42)
	at scala.collection.Iterator$class.foreach(Iterator.scala:893)
	at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
	at org.apache.spark.rdd.AsyncRDDActions$$anonfun$foreachAsync$1$$anonfun$apply$12.apply(AsyncRDDActions.scala:127)
	at org.apache.spark.rdd.AsyncRDDActions$$anonfun$foreachAsync$1$$anonfun$apply$12.apply(AsyncRDDActions.scala:127)
	at org.apache.spark.SparkContext$$anonfun$33.apply(SparkContext.scala:1974)
	at org.apache.spark.SparkContext$$anonfun$33.apply(SparkContext.scala:1974)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
	at org.apache.spark.scheduler.Task.run(Task.scala:85)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
	at java.lang.Thread.run(Thread.java:745) 

I think DataOutputBuffer isn't cleared on time cause this problem.



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