[jira] [Created] (SPARK-10341) SMJ fail with unable to acquire memory

["Davies Liu (JIRA)" <ji...@apache.org>]

Davies Liu created SPARK-10341:
----------------------------------

             Summary: SMJ fail with unable to acquire memory
                 Key: SPARK-10341
                 URL: https://issues.apache.org/jira/browse/SPARK-10341
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 1.5.0
            Reporter: Davies Liu
            Assignee: Davies Liu
            Priority: Critical


In SMJ, the first ExternalSorter could consume all the memory before spilling, then the second can not even acquire the first page.

{code}
ava.io.IOException: Unable to acquire 16777216 bytes of memory
	at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPage(UnsafeExternalSorter.java:368)
	at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.<init>(UnsafeExternalSorter.java:138)
	at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.create(UnsafeExternalSorter.java:106)
	at org.apache.spark.sql.execution.UnsafeExternalRowSorter.<init>(UnsafeExternalRowSorter.java:68)
	at org.apache.spark.sql.execution.TungstenSort.org$apache$spark$sql$execution$TungstenSort$$preparePartition$1(sort.scala:146)
	at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169)
	at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169)
	at org.apache.spark.rdd.MapPartitionsWithPreparationRDD.compute(MapPartitionsWithPreparationRDD.scala:45)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
	at org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:88)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
	at org.apache.spark.scheduler.Task.run(Task.scala:88)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
{code}



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