[jira] [Commented] (SPARK-17450) spark sql rownumber OOM
[
https://issues.apache.org/jira/browse/SPARK-17450?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15675639#comment-15675639
]
cen yuhai commented on SPARK-17450:
---
I will upgrade to 2.x, please close this issue
> 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$$anon$11.next(Iterator.scala:328)
> at
> org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.processInputs(TungstenAggregationIterator.scala:512)
> at
> org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.(TungstenAggregationIterator.scala:686)
> at
> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$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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[jira] [Commented] (SPARK-17450) spark sql rownumber OOM
[
https://issues.apache.org/jira/browse/SPARK-17450?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15671411#comment-15671411
]
Herman van Hovell commented on SPARK-17450:
---
[~cenyuhai] did you have any luck with merging these? Can we close this issue?
> 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.(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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[jira] [Commented] (SPARK-17450) spark sql rownumber OOM
[
https://issues.apache.org/jira/browse/SPARK-17450?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15488005#comment-15488005
]
Herman van Hovell commented on SPARK-17450:
---
https://github.com/apache/spark/pull/10605
> 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$$anon$11.next(Iterator.scala:328)
> at
> org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.processInputs(TungstenAggregationIterator.scala:512)
> at
> org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.(TungstenAggregationIterator.scala:686)
> at
> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$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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To
[jira] [Commented] (SPARK-17450) spark sql rownumber OOM
[
https://issues.apache.org/jira/browse/SPARK-17450?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15479097#comment-15479097
]
cen yuhai commented on SPARK-17450:
---
can you provide me davies's pr?
> 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.(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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[jira] [Commented] (SPARK-17450) spark sql rownumber OOM
[
https://issues.apache.org/jira/browse/SPARK-17450?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15474517#comment-15474517
]
Herman van Hovell commented on SPARK-17450:
---
You could try. You would also have to add the follow-up by davies (that adds
the spilling logic).
> 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$$anon$11.next(Iterator.scala:328)
> at
> org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.processInputs(TungstenAggregationIterator.scala:512)
> at
> org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.(TungstenAggregationIterator.scala:686)
> at
> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$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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[jira] [Commented] (SPARK-17450) spark sql rownumber OOM
[
https://issues.apache.org/jira/browse/SPARK-17450?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15474518#comment-15474518
]
Herman van Hovell commented on SPARK-17450:
---
You could try. You would also have to add the follow-up by davies (that adds
the spilling logic).
> 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.(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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[jira] [Commented] (SPARK-17450) spark sql rownumber OOM
[
https://issues.apache.org/jira/browse/SPARK-17450?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15474430#comment-15474430
]
cen yuhai commented on SPARK-17450:
---
hiļ¼herman, can i merge your pr for native spark window function?
https://github.com/apache/spark/pull/9819 ???
> 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$$anon$11.next(Iterator.scala:328)
> at
> org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.processInputs(TungstenAggregationIterator.scala:512)
> at
> org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.(TungstenAggregationIterator.scala:686)
> at
> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$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}
--
This message was sent by Atlassian JIRA
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[jira] [Commented] (SPARK-17450) spark sql rownumber OOM
[
https://issues.apache.org/jira/browse/SPARK-17450?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15474363#comment-15474363
]
Herman van Hovell commented on SPARK-17450:
---
This generally a bad idea. All your data is moved to a single executor, sorted
in that executor and finally processes on that executor. So I am curious to
know what the use case is.
What makes things worse is the fact that 1.6 keeps all records in a single
partition in memory; causing an OOM in your case. In 2.0 we spill records to
disk (above 4000 records) in order to prevent OOMs. Could you try this in 2.0?
> 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.(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])
>
