[
https://issues.apache.org/jira/browse/SPARK-27558?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Alessandro Bellina updated SPARK-27558:
---------------------------------------
Description:
We see an NPE in the UnsafeInMemorySorter.getMemoryUsage function (due to the
array we are accessing there being null). This looks to be caused by a Spark
OOM when UnsafeInMemorySorter is trying to spill.
This is likely a symptom of https://issues.apache.org/jira/browse/SPARK-21492.
The real question for this ticket is, could we handle things more gracefully,
rather than NPE. For example:
Remove this:
https://github.com/apache/spark/blob/master/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java#L182
so when this fails (and store the new array into a temporary):
https://github.com/apache/spark/blob/master/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java#L186
we don't end up with a null "array". This state is causing one of our jobs to
hang infinitely (we think) due to the original allocation error.
Stack trace for reference
{noformat}
2019-04-23 08:57:14,989 [Executor task launch worker for task 46729] ERROR
org.apache.spark.TaskContextImpl - Error in TaskCompletionListener
java.lang.NullPointerException
at
org.apache.spark.util.collection.unsafe.sort.UnsafeInMemorySorter.getMemoryUsage(UnsafeInMemorySorter.java:208)
at
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.getMemoryUsage(UnsafeExternalSorter.java:249)
at
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.updatePeakMemoryUsed(UnsafeExternalSorter.java:253)
at
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.freeMemory(UnsafeExternalSorter.java:296)
at
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.cleanupResources(UnsafeExternalSorter.java:328)
at
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.lambda$new$0(UnsafeExternalSorter.java:178)
at
org.apache.spark.TaskContextImpl$$anonfun$markTaskCompleted$1.apply(TaskContextImpl.scala:118)
at
org.apache.spark.TaskContextImpl$$anonfun$markTaskCompleted$1.apply(TaskContextImpl.scala:118)
at
org.apache.spark.TaskContextImpl$$anonfun$invokeListeners$1.apply(TaskContextImpl.scala:131)
at
org.apache.spark.TaskContextImpl$$anonfun$invokeListeners$1.apply(TaskContextImpl.scala:129)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at
org.apache.spark.TaskContextImpl.invokeListeners(TaskContextImpl.scala:129)
at
org.apache.spark.TaskContextImpl.markTaskCompleted(TaskContextImpl.scala:117)
at org.apache.spark.scheduler.Task.run(Task.scala:119)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
2019-04-23 08:57:15,069 [Executor task launch worker for task 46729] ERROR
org.apache.spark.executor.Executor - Exception in task 102.0 in stage 28.0
(TID 46729)
org.apache.spark.util.TaskCompletionListenerException: null
Previous exception in task: Unable to acquire 65536 bytes of memory, got 0
org.apache.spark.memory.MemoryConsumer.throwOom(MemoryConsumer.java:157)
org.apache.spark.memory.MemoryConsumer.allocateArray(MemoryConsumer.java:98)
org.apache.spark.util.collection.unsafe.sort.UnsafeInMemorySorter.reset(UnsafeInMemorySorter.java:186)
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.spill(UnsafeExternalSorter.java:229)
org.apache.spark.memory.TaskMemoryManager.acquireExecutionMemory(TaskMemoryManager.java:204)
org.apache.spark.memory.TaskMemoryManager.allocatePage(TaskMemoryManager.java:283)
org.apache.spark.memory.MemoryConsumer.allocateArray(MemoryConsumer.java:96)
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.growPointerArrayIfNecessary(UnsafeExternalSorter.java:348)
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.insertRecord(UnsafeExternalSorter.java:403)
org.apache.spark.sql.execution.UnsafeExternalRowSorter.insertRow(UnsafeExternalRowSorter.java:135)
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage23.sort_addToSorter_0$(Unknown
Source)
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage23.processNext(Unknown
Source)
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:614)
org.apache.spark.sql.execution.window.WindowExec$$anonfun$11$$anon$1.fetchNextRow(WindowExec.scala:314)
org.apache.spark.sql.execution.window.WindowExec$$anonfun$11$$anon$1.<init>(WindowExec.scala:323)
org.apache.spark.sql.execution.window.WindowExec$$anonfun$11.apply(WindowExec.scala:303)
org.apache.spark.sql.execution.window.WindowExec$$anonfun$11.apply(WindowExec.scala:302)
org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:801)
org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:801)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
org.apache.spark.scheduler.Task.run(Task.scala:109)
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
java.lang.Thread.run(Thread.java:748)
at
org.apache.spark.TaskContextImpl.invokeListeners(TaskContextImpl.scala:139)
at
org.apache.spark.TaskContextImpl.markTaskCompleted(TaskContextImpl.scala:117)
at org.apache.spark.scheduler.Task.run(Task.scala:119)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
{noformat}
was:
We see an NPE in the UnsafeInMemorySorter.getMemoryUsage function (due to the
array we are accessing there being null). This looks to be caused by a Spark
OOM when UnsafeInMemorySorter is trying to spill.
This is likely a symptom of https://issues.apache.org/jira/browse/SPARK-21492.
The real question for this ticket is, could we handle things more gracefully,
rather than NPE. For example:
allocate the new array into a temporary here:
https://github.com/apache/spark/blob/master/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java#L182
so when this fails:
https://github.com/apache/spark/blob/master/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java#L186
we don't end up with a null "array". This state is causing one of our jobs to
hang infinitely (we think) due to the original allocation error.
Stack trace for reference
{noformat}
2019-04-23 08:57:14,989 [Executor task launch worker for task 46729] ERROR
org.apache.spark.TaskContextImpl - Error in TaskCompletionListener
java.lang.NullPointerException
at
org.apache.spark.util.collection.unsafe.sort.UnsafeInMemorySorter.getMemoryUsage(UnsafeInMemorySorter.java:208)
at
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.getMemoryUsage(UnsafeExternalSorter.java:249)
at
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.updatePeakMemoryUsed(UnsafeExternalSorter.java:253)
at
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.freeMemory(UnsafeExternalSorter.java:296)
at
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.cleanupResources(UnsafeExternalSorter.java:328)
at
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.lambda$new$0(UnsafeExternalSorter.java:178)
at
org.apache.spark.TaskContextImpl$$anonfun$markTaskCompleted$1.apply(TaskContextImpl.scala:118)
at
org.apache.spark.TaskContextImpl$$anonfun$markTaskCompleted$1.apply(TaskContextImpl.scala:118)
at
org.apache.spark.TaskContextImpl$$anonfun$invokeListeners$1.apply(TaskContextImpl.scala:131)
at
org.apache.spark.TaskContextImpl$$anonfun$invokeListeners$1.apply(TaskContextImpl.scala:129)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at
org.apache.spark.TaskContextImpl.invokeListeners(TaskContextImpl.scala:129)
at
org.apache.spark.TaskContextImpl.markTaskCompleted(TaskContextImpl.scala:117)
at org.apache.spark.scheduler.Task.run(Task.scala:119)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
2019-04-23 08:57:15,069 [Executor task launch worker for task 46729] ERROR
org.apache.spark.executor.Executor - Exception in task 102.0 in stage 28.0
(TID 46729)
org.apache.spark.util.TaskCompletionListenerException: null
Previous exception in task: Unable to acquire 65536 bytes of memory, got 0
org.apache.spark.memory.MemoryConsumer.throwOom(MemoryConsumer.java:157)
org.apache.spark.memory.MemoryConsumer.allocateArray(MemoryConsumer.java:98)
org.apache.spark.util.collection.unsafe.sort.UnsafeInMemorySorter.reset(UnsafeInMemorySorter.java:186)
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.spill(UnsafeExternalSorter.java:229)
org.apache.spark.memory.TaskMemoryManager.acquireExecutionMemory(TaskMemoryManager.java:204)
org.apache.spark.memory.TaskMemoryManager.allocatePage(TaskMemoryManager.java:283)
org.apache.spark.memory.MemoryConsumer.allocateArray(MemoryConsumer.java:96)
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.growPointerArrayIfNecessary(UnsafeExternalSorter.java:348)
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.insertRecord(UnsafeExternalSorter.java:403)
org.apache.spark.sql.execution.UnsafeExternalRowSorter.insertRow(UnsafeExternalRowSorter.java:135)
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage23.sort_addToSorter_0$(Unknown
Source)
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage23.processNext(Unknown
Source)
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:614)
org.apache.spark.sql.execution.window.WindowExec$$anonfun$11$$anon$1.fetchNextRow(WindowExec.scala:314)
org.apache.spark.sql.execution.window.WindowExec$$anonfun$11$$anon$1.<init>(WindowExec.scala:323)
org.apache.spark.sql.execution.window.WindowExec$$anonfun$11.apply(WindowExec.scala:303)
org.apache.spark.sql.execution.window.WindowExec$$anonfun$11.apply(WindowExec.scala:302)
org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:801)
org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:801)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
org.apache.spark.scheduler.Task.run(Task.scala:109)
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
java.lang.Thread.run(Thread.java:748)
at
org.apache.spark.TaskContextImpl.invokeListeners(TaskContextImpl.scala:139)
at
org.apache.spark.TaskContextImpl.markTaskCompleted(TaskContextImpl.scala:117)
at org.apache.spark.scheduler.Task.run(Task.scala:119)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
{noformat}
> NPE in TaskCompletionListener due to Spark OOM in UnsafeExternalSorter
> causing tasks to hang
> --------------------------------------------------------------------------------------------
>
> Key: SPARK-27558
> URL: https://issues.apache.org/jira/browse/SPARK-27558
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.3.3, 2.4.2
> Reporter: Alessandro Bellina
> Priority: Critical
>
> We see an NPE in the UnsafeInMemorySorter.getMemoryUsage function (due to the
> array we are accessing there being null). This looks to be caused by a Spark
> OOM when UnsafeInMemorySorter is trying to spill.
> This is likely a symptom of
> https://issues.apache.org/jira/browse/SPARK-21492. The real question for this
> ticket is, could we handle things more gracefully, rather than NPE. For
> example:
> Remove this:
> https://github.com/apache/spark/blob/master/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java#L182
> so when this fails (and store the new array into a temporary):
> https://github.com/apache/spark/blob/master/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java#L186
> we don't end up with a null "array". This state is causing one of our jobs to
> hang infinitely (we think) due to the original allocation error.
> Stack trace for reference
> {noformat}
> 2019-04-23 08:57:14,989 [Executor task launch worker for task 46729] ERROR
> org.apache.spark.TaskContextImpl - Error in TaskCompletionListener
> java.lang.NullPointerException
> at
> org.apache.spark.util.collection.unsafe.sort.UnsafeInMemorySorter.getMemoryUsage(UnsafeInMemorySorter.java:208)
> at
> org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.getMemoryUsage(UnsafeExternalSorter.java:249)
> at
> org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.updatePeakMemoryUsed(UnsafeExternalSorter.java:253)
> at
> org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.freeMemory(UnsafeExternalSorter.java:296)
> at
> org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.cleanupResources(UnsafeExternalSorter.java:328)
> at
> org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.lambda$new$0(UnsafeExternalSorter.java:178)
> at
> org.apache.spark.TaskContextImpl$$anonfun$markTaskCompleted$1.apply(TaskContextImpl.scala:118)
> at
> org.apache.spark.TaskContextImpl$$anonfun$markTaskCompleted$1.apply(TaskContextImpl.scala:118)
> at
> org.apache.spark.TaskContextImpl$$anonfun$invokeListeners$1.apply(TaskContextImpl.scala:131)
> at
> org.apache.spark.TaskContextImpl$$anonfun$invokeListeners$1.apply(TaskContextImpl.scala:129)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> org.apache.spark.TaskContextImpl.invokeListeners(TaskContextImpl.scala:129)
> at
> org.apache.spark.TaskContextImpl.markTaskCompleted(TaskContextImpl.scala:117)
> at org.apache.spark.scheduler.Task.run(Task.scala:119)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748)
> 2019-04-23 08:57:15,069 [Executor task launch worker for task 46729] ERROR
> org.apache.spark.executor.Executor - Exception in task 102.0 in stage 28.0
> (TID 46729)
> org.apache.spark.util.TaskCompletionListenerException: null
> Previous exception in task: Unable to acquire 65536 bytes of memory, got 0
> org.apache.spark.memory.MemoryConsumer.throwOom(MemoryConsumer.java:157)
>
> org.apache.spark.memory.MemoryConsumer.allocateArray(MemoryConsumer.java:98)
>
> org.apache.spark.util.collection.unsafe.sort.UnsafeInMemorySorter.reset(UnsafeInMemorySorter.java:186)
>
> org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.spill(UnsafeExternalSorter.java:229)
>
> org.apache.spark.memory.TaskMemoryManager.acquireExecutionMemory(TaskMemoryManager.java:204)
>
> org.apache.spark.memory.TaskMemoryManager.allocatePage(TaskMemoryManager.java:283)
>
> org.apache.spark.memory.MemoryConsumer.allocateArray(MemoryConsumer.java:96)
>
> org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.growPointerArrayIfNecessary(UnsafeExternalSorter.java:348)
>
> org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.insertRecord(UnsafeExternalSorter.java:403)
>
> org.apache.spark.sql.execution.UnsafeExternalRowSorter.insertRow(UnsafeExternalRowSorter.java:135)
>
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage23.sort_addToSorter_0$(Unknown
> Source)
>
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage23.processNext(Unknown
> Source)
>
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
>
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$1.hasNext(WholeStageCodegenExec.scala:614)
>
> org.apache.spark.sql.execution.window.WindowExec$$anonfun$11$$anon$1.fetchNextRow(WindowExec.scala:314)
>
> org.apache.spark.sql.execution.window.WindowExec$$anonfun$11$$anon$1.<init>(WindowExec.scala:323)
>
> org.apache.spark.sql.execution.window.WindowExec$$anonfun$11.apply(WindowExec.scala:303)
>
> org.apache.spark.sql.execution.window.WindowExec$$anonfun$11.apply(WindowExec.scala:302)
>
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:801)
>
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:801)
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
>
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
>
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
> org.apache.spark.scheduler.Task.run(Task.scala:109)
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
>
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
>
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> java.lang.Thread.run(Thread.java:748)
> at
> org.apache.spark.TaskContextImpl.invokeListeners(TaskContextImpl.scala:139)
> at
> org.apache.spark.TaskContextImpl.markTaskCompleted(TaskContextImpl.scala:117)
> at org.apache.spark.scheduler.Task.run(Task.scala:119)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748)
> {noformat}
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