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https://issues.apache.org/jira/browse/SPARK-13450?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15863275#comment-15863275
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Apache Spark commented on SPARK-13450:
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User 'tejasapatil' has created a pull request for this issue:
https://github.com/apache/spark/pull/16909
> SortMergeJoin will OOM when join rows have lot of same keys
> -----------------------------------------------------------
>
> Key: SPARK-13450
> URL: https://issues.apache.org/jira/browse/SPARK-13450
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.0, 2.0.2, 2.1.0
> Reporter: Hong Shen
> Attachments: heap-dump-analysis.png
>
>
> When I run a sql with join, task throw java.lang.OutOfMemoryError and sql
> failed. I have set spark.executor.memory 4096m.
> SortMergeJoin use a ArrayBuffer[InternalRow] to store bufferedMatches, if
> the join rows have a lot of same key, it will throw OutOfMemoryError.
> {code}
> /** Buffered rows from the buffered side of the join. This is empty if
> there are no matches. */
> private[this] val bufferedMatches: ArrayBuffer[InternalRow] = new
> ArrayBuffer[InternalRow]
> {code}
> Here is the stackTrace:
> {code}
> org.xerial.snappy.SnappyNative.arrayCopy(Native Method)
> org.xerial.snappy.Snappy.arrayCopy(Snappy.java:84)
> org.xerial.snappy.SnappyInputStream.rawRead(SnappyInputStream.java:190)
> org.xerial.snappy.SnappyInputStream.read(SnappyInputStream.java:163)
> java.io.DataInputStream.readFully(DataInputStream.java:195)
> java.io.DataInputStream.readLong(DataInputStream.java:416)
> org.apache.spark.util.collection.unsafe.sort.UnsafeSorterSpillReader.loadNext(UnsafeSorterSpillReader.java:71)
> org.apache.spark.util.collection.unsafe.sort.UnsafeSorterSpillMerger$2.loadNext(UnsafeSorterSpillMerger.java:79)
> org.apache.spark.sql.execution.UnsafeExternalRowSorter$1.next(UnsafeExternalRowSorter.java:136)
> org.apache.spark.sql.execution.UnsafeExternalRowSorter$1.next(UnsafeExternalRowSorter.java:123)
> org.apache.spark.sql.execution.RowIteratorFromScala.advanceNext(RowIterator.scala:84)
> org.apache.spark.sql.execution.joins.SortMergeJoinScanner.advancedBufferedToRowWithNullFreeJoinKey(SortMergeJoin.scala:300)
> org.apache.spark.sql.execution.joins.SortMergeJoinScanner.bufferMatchingRows(SortMergeJoin.scala:329)
> org.apache.spark.sql.execution.joins.SortMergeJoinScanner.findNextInnerJoinRows(SortMergeJoin.scala:229)
> org.apache.spark.sql.execution.joins.SortMergeJoin$$anonfun$doExecute$1$$anon$1.advanceNext(SortMergeJoin.scala:105)
> org.apache.spark.sql.execution.RowIteratorToScala.hasNext(RowIterator.scala:68)
> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:88)
> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:86)
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:741)
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:741)
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:337)
> org.apache.spark.rdd.RDD.iterator(RDD.scala:301)
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:337)
> org.apache.spark.rdd.RDD.iterator(RDD.scala:301)
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> org.apache.spark.scheduler.Task.run(Task.scala:89)
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:215)
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> java.lang.Thread.run(Thread.java:744)
> {code}
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