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Posted to issues@spark.apache.org by "Herman van Hovell (JIRA)" <ji...@apache.org> on 2016/11/18 08:57:58 UTC
[jira] [Closed] (SPARK-17450) spark sql rownumber OOM
[ https://issues.apache.org/jira/browse/SPARK-17450?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Herman van Hovell closed SPARK-17450.
-------------------------------------
Resolution: Not A Problem
> spark sql rownumber OOM
> -----------------------
>
> Key: SPARK-17450
> URL: https://issues.apache.org/jira/browse/SPARK-17450
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.2
> Reporter: cen yuhai
>
> spark sql will be OOM when using row_number() over too much sorted records... There will be only 1 task to handle all records
> This sql group by passenger_id, we have 100 million passengers.
> {code}
> SELECT
> passenger_id,
> total_order,
> (CASE WHEN row_number() over (ORDER BY total_order DESC) BETWEEN 0 AND 670800 THEN 'V3' END) AS order_rank
> FROM
> (
> SELECT
> passenger_id,
> 1 as total_order
> FROM table
> GROUP BY passenger_id
> ) dd1
> {code}
> {code}
> java.lang.OutOfMemoryError: Java heap space
> at org.apache.spark.sql.catalyst.expressions.UnsafeRow.copy(UnsafeRow.java:536)
> at org.apache.spark.sql.catalyst.expressions.UnsafeRow.copy(UnsafeRow.java:93)
> at org.apache.spark.sql.execution.Window$$anonfun$8$$anon$1.fetchNextPartition(Window.scala:278)
> at org.apache.spark.sql.execution.Window$$anonfun$8$$anon$1.next(Window.scala:304)
> at org.apache.spark.sql.execution.Window$$anonfun$8$$anon$1.next(Window.scala:246)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.processInputs(TungstenAggregationIterator.scala:512)
> at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.<init>(TungstenAggregationIterator.scala:686)
> at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:95)
> at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:86)
> at org.apache.spark.rdd.RDD$$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}
> physical plan:
> {code}
> == Physical Plan ==
> Project [passenger_id#7L,total_order#0,CASE WHEN ((_we0#20 >= 0) && (_we1#21 <= 670800)) THEN V3 AS order_rank#1]
> +- Window [passenger_id#7L,total_order#0], [HiveWindowFunction#org.apache.hadoop.hive.ql.udf.generic.GenericUDAFRowNumber() windowspecdefinition(total_order#0 DESC,ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS _we0#20,HiveWindowFunction#org.apache.hadoop.hive.ql.udf.generic.GenericUDAFRowNumber() windowspecdefinition(total_order#0 DESC,ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS _we1#21], [total_order#0 DESC]
> +- Sort [total_order#0 DESC], false, 0
> +- TungstenExchange SinglePartition, None
> +- Project [passenger_id#7L,total_order#0]
> +- TungstenAggregate(key=[passenger_id#7L], functions=[], output=[passenger_id#7L,total_order#0])
> +- TungstenExchange hashpartitioning(passenger_id#7L,1000), None
> +- TungstenAggregate(key=[passenger_id#7L], functions=[], output=[passenger_id#7L])
> +- Project [passenger_id#7L]
> +- Filter product#9 IN (kuai,gulf)
> +- HiveTableScan [passenger_id#7L,product#9], MetastoreRelation pbs_dw, dwv_order_whole_day, None,
> {code}
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