[
https://issues.apache.org/jira/browse/SPARK-17450?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
cen yuhai updated SPARK-17450:
------------------------------
Description:
spark sql will be OOM when using row_number() over too much sorted records...
There will be only 1 task to handle all records
{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:
plan
== 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,
[(concat(YEAR#3,-,MONTH#4,-,DAY#5) >=
2016-07-01),(concat(YEAR#3,-,MONTH#4,-,DAY#5) <= 2016-07-31)]
was:
spark sql will be OOM when using row_number() over too much sorted records...
There will be only 1 task to handle all records
{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}
> 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
> {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:
> plan
> == 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,
> [(concat(YEAR#3,-,MONTH#4,-,DAY#5) >=
> 2016-07-01),(concat(YEAR#3,-,MONTH#4,-,DAY#5) <= 2016-07-31)]
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