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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-10474) Aggregation failed with unable to acquire memory
Date Thu, 17 Sep 2015 13:26:04 GMT

     [ https://issues.apache.org/jira/browse/SPARK-10474?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-10474:
------------------------------------

    Assignee: Apache Spark

> Aggregation failed with unable to acquire memory
> ------------------------------------------------
>
>                 Key: SPARK-10474
>                 URL: https://issues.apache.org/jira/browse/SPARK-10474
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 1.5.0
>            Reporter: Yi Zhou
>            Assignee: Apache Spark
>            Priority: Blocker
>
> In aggregation case, a  Lost task happened with below error.
> {code}
>  java.io.IOException: Could not acquire 65536 bytes of memory
>         at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.initializeForWriting(UnsafeExternalSorter.java:169)
>         at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.spill(UnsafeExternalSorter.java:220)
>         at org.apache.spark.sql.execution.UnsafeKVExternalSorter.<init>(UnsafeKVExternalSorter.java:126)
>         at org.apache.spark.sql.execution.UnsafeFixedWidthAggregationMap.destructAndCreateExternalSorter(UnsafeFixedWidthAggregationMap.java:257)
>         at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.switchToSortBasedAggregation(TungstenAggregationIterator.scala:435)
>         at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.processInputs(TungstenAggregationIterator.scala:379)
>         at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.start(TungstenAggregationIterator.scala:622)
>         at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1.org$apache$spark$sql$execution$aggregate$TungstenAggregate$$anonfun$$executePartition$1(TungstenAggregate.scala:110)
>         at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:119)
>         at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:119)
>         at org.apache.spark.rdd.MapPartitionsWithPreparationRDD.compute(MapPartitionsWithPreparationRDD.scala:64)
>         at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>         at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>         at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>         at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>         at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>         at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
>         at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
>         at org.apache.spark.scheduler.Task.run(Task.scala:88)
>         at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
>         at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>         at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>         at java.lang.Thread.run(Thread.java:745)
> {code}
> Key SQL Query
> {code:sql}
> INSERT INTO TABLE test_table
> SELECT
>   ss.ss_customer_sk AS cid,
>   count(CASE WHEN i.i_class_id=1  THEN 1 ELSE NULL END) AS id1,
>   count(CASE WHEN i.i_class_id=3  THEN 1 ELSE NULL END) AS id3,
>   count(CASE WHEN i.i_class_id=5  THEN 1 ELSE NULL END) AS id5,
>   count(CASE WHEN i.i_class_id=7  THEN 1 ELSE NULL END) AS id7,
>   count(CASE WHEN i.i_class_id=9  THEN 1 ELSE NULL END) AS id9,
>   count(CASE WHEN i.i_class_id=11 THEN 1 ELSE NULL END) AS id11,
>   count(CASE WHEN i.i_class_id=13 THEN 1 ELSE NULL END) AS id13,
>   count(CASE WHEN i.i_class_id=15 THEN 1 ELSE NULL END) AS id15,
>   count(CASE WHEN i.i_class_id=2  THEN 1 ELSE NULL END) AS id2,
>   count(CASE WHEN i.i_class_id=4  THEN 1 ELSE NULL END) AS id4,
>   count(CASE WHEN i.i_class_id=6  THEN 1 ELSE NULL END) AS id6,
>   count(CASE WHEN i.i_class_id=8  THEN 1 ELSE NULL END) AS id8,
>   count(CASE WHEN i.i_class_id=10 THEN 1 ELSE NULL END) AS id10,
>   count(CASE WHEN i.i_class_id=14 THEN 1 ELSE NULL END) AS id14,
>   count(CASE WHEN i.i_class_id=16 THEN 1 ELSE NULL END) AS id16
> FROM store_sales ss
> INNER JOIN item i ON ss.ss_item_sk = i.i_item_sk
> WHERE i.i_category IN ('Books')
> AND ss.ss_customer_sk IS NOT NULL
> GROUP BY ss.ss_customer_sk
> HAVING count(ss.ss_item_sk) > 5
> {code}
> Note:
> the store_sales is a big fact table and item is a small dimension table.



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