[jira] [Created] (SPARK-17488) TakeAndOrder will OOM when the data is very large

["cen yuhai (JIRA)" <ji...@apache.org>]

cen yuhai created SPARK-17488:
---------------------------------

             Summary: TakeAndOrder will OOM when the data is very large
                 Key: SPARK-17488
                 URL: https://issues.apache.org/jira/browse/SPARK-17488
             Project: Spark
          Issue Type: Bug
          Components: Spark Core, SQL
    Affects Versions: 2.0.0, 1.6.2
         Environment: spark1.6
            Reporter: cen yuhai


In function Utils.takeOrdered, it will sort all data in memory, when the data is very large, It will OOM.
{code}
16/08/22 16:18:42 ERROR executor.Executor: Exception in task 139.0 in stage 5.0 (TID 6420)
java.lang.OutOfMemoryError: Java heap space
        at org.spark-project.guava.collect.Ordering.leastOf(Ordering.java:657)
        at org.apache.spark.util.collection.Utils$.takeOrdered(Utils.scala:37)
        at org.apache.spark.sql.execution.TakeOrderedAndProject$$anonfun$9.apply(basicOperators.scala:227)
        at org.apache.spark.sql.execution.TakeOrderedAndProject$$anonfun$9.apply(basicOperators.scala:226)
        at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)
        at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)
        at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
        at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
        at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:74)
        at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
        at org.apache.spark.scheduler.Task.run(Task.scala:104)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:247)
        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) 
{code}



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