[HIVE-16004] OutOfMemory in SparkReduceRecordHandler with vectorization mode

• Type: Bug
• Status: Resolved
• Priority: Major
• Resolution: Fixed
• Fix Version/s: 2.3.0
• Assignee: Colin
• Reporter: Colin
• Created: 22/Feb/17 06:30
• Updated: 21/Jul/17 18:45
• Resolved: 24/Feb/17 02:26

Description

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.