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From "Matthew Scheifer (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-20086) issue with pyspark 2.1.0 window function
Date Fri, 14 Jul 2017 19:32:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-20086?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16087862#comment-16087862
] 

Matthew Scheifer commented on SPARK-20086:
------------------------------------------

Is there a way I can work around this if I'm stuck on Spark 2.1.0 ?

Thanks,
Matthew

> issue with pyspark 2.1.0 window function
> ----------------------------------------
>
>                 Key: SPARK-20086
>                 URL: https://issues.apache.org/jira/browse/SPARK-20086
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark
>    Affects Versions: 2.1.0
>            Reporter: mandar uapdhye
>            Assignee: Herman van Hovell
>             Fix For: 2.1.1, 2.2.0
>
>
> original  post at
> [stackoverflow | http://stackoverflow.com/questions/43007433/pyspark-2-1-0-error-when-working-with-window-function]
> I get error when working with pyspark window function. here is some example code:
> {code:title=borderStyle=solid}
>     import pyspark
>     import pyspark.sql.functions as sf
>     import pyspark.sql.types as sparktypes
>     from pyspark.sql import window
>     
>     sc = pyspark.SparkContext()
>     sqlc = pyspark.SQLContext(sc)
>     rdd = sc.parallelize([(1, 2.0), (1, 3.0), (1, 1.), (1, -2.), (1, -1.)])
>     df = sqlc.createDataFrame(rdd, ["x", "AmtPaid"])
>     df.show()
> {code}
> gives:
>     |  x|AmtPaid|
>     |  1|    2.0|
>     |  1|    3.0|
>     |  1|    1.0|
>     |  1|   -2.0|
>     |  1|   -1.0|
> next, compute cumulative sum
> {code:title=test.py|borderStyle=solid}
>     win_spec_max = (window.Window
>                     .partitionBy(['x'])
>                     .rowsBetween(window.Window.unboundedPreceding, 0)))
>     df = df.withColumn('AmtPaidCumSum',
>                        sf.sum(sf.col('AmtPaid')).over(win_spec_max))
>     df.show()
> {code}
> gives,
>     |  x|AmtPaid|AmtPaidCumSum|
>     |  1|    2.0|          2.0|
>     |  1|    3.0|          5.0|
>     |  1|    1.0|          6.0|
>     |  1|   -2.0|          4.0|
>     |  1|   -1.0|          3.0|
> next, compute cumulative max,
> {code}
>     df = df.withColumn('AmtPaidCumSumMax', sf.max(sf.col('AmtPaidCumSum')).over(win_spec_max))
>     df.show()
> {code}
> gives error log
> {noformat}
>      Py4JJavaError: An error occurred while calling o2609.showString.
> with traceback:
>     Py4JJavaErrorTraceback (most recent call last)
>     <ipython-input-215-3106d06b6e49> in <module>()
>     ----> 1 df.show()
>     /Users/<>/spark-2.1.0-bin-hadoop2.7/python/pyspark/sql/dataframe.pyc in show(self,
n, truncate)
>         316         """
>         317         if isinstance(truncate, bool) and truncate:
>     --> 318             print(self._jdf.showString(n, 20))
>         319         else:
>         320             print(self._jdf.showString(n, int(truncate)))
>     /Users/<>/.virtualenvs/<>/lib/python2.7/site-packages/py4j/java_gateway.pyc
in __call__(self, *args)
>        1131         answer = self.gateway_client.send_command(command)
>        1132         return_value = get_return_value(
>     -> 1133             answer, self.gateway_client, self.target_id, self.name)
>        1134 
>        1135         for temp_arg in temp_args:
>     /Users/<>/spark-2.1.0-bin-hadoop2.7/python/pyspark/sql/utils.pyc in deco(*a,
**kw)
>          61     def deco(*a, **kw):
>          62         try:
>     ---> 63             return f(*a, **kw)
>          64         except py4j.protocol.Py4JJavaError as e:
>          65             s = e.java_exception.toString()
>     /Users/<>/.virtualenvs/<>/lib/python2.7/site-packages/py4j/protocol.pyc
in get_return_value(answer, gateway_client, target_id, name)
>         317                 raise Py4JJavaError(
>         318                     "An error occurred while calling {0}{1}{2}.\n".
>     --> 319                     format(target_id, ".", name), value)
>         320             else:
>         321                 raise Py4JError(
> {noformat}
> but interestingly enough, if i introduce another change before sencond window operation,
say inserting a column then it does not give that error:
> {code}
>     df = df.withColumn('MaxBound', sf.lit(6.))
>     df.show()
> {code}
>     |  x|AmtPaid|AmtPaidCumSum|MaxBound|
>     |  1|    2.0|          2.0|     6.0|
>     |  1|    3.0|          5.0|     6.0|
>     |  1|    1.0|          6.0|     6.0|
>     |  1|   -2.0|          4.0|     6.0|
>     |  1|   -1.0|          3.0|     6.0|
> {code}
>     #then apply the second window operations
>     df = df.withColumn('AmtPaidCumSumMax', sf.max(sf.col('AmtPaidCumSum')).over(win_spec_max))
>     df.show()
> {code}
>     |  x|AmtPaid|AmtPaidCumSum|MaxBound|AmtPaidCumSumMax|
>     |  1|    2.0|          2.0|     6.0|             2.0|
>     |  1|    3.0|          5.0|     6.0|             5.0|
>     |  1|    1.0|          6.0|     6.0|             6.0|
>     |  1|   -2.0|          4.0|     6.0|             6.0|
>     |  1|   -1.0|          3.0|     6.0|             6.0|
> I do not understand this behaviour
> well, so far so good, but then I try another operation then again get similar error:
> {code}
>     def _udf_compare_cumsum_sll(x):
>         if x['AmtPaidCumSumMax'] >= x['MaxBound']:
>             output = 0
>         else:
>             output = x['AmtPaid']
>         return output
>     udf_compare_cumsum_sll = sf.udf(_udf_compare_cumsum_sll, sparktypes.FloatType())
>     df = df.withColumn('AmtPaidAdjusted', udf_compare_cumsum_sll(sf.struct([df[x] for
x in df.columns])))
>     df.show()
> {code}
> gives,
> {noformat}
>     Py4JJavaErrorTraceback (most recent call last)
>     <ipython-input-18-3106d06b6e49> in <module>()
>     ----> 1 df.show()
>     /Users/<>/spark-2.1.0-bin-hadoop2.7/python/pyspark/sql/dataframe.pyc in show(self,
n, truncate)
>         316         """
>         317         if isinstance(truncate, bool) and truncate:
>     --> 318             print(self._jdf.showString(n, 20))
>         319         else:
>         320             print(self._jdf.showString(n, int(truncate)))
>     /Users/<>/.virtualenvs/<>/lib/python2.7/site-packages/py4j/java_gateway.pyc
in __call__(self, *args)
>        1131         answer = self.gateway_client.send_command(command)
>        1132         return_value = get_return_value(
>     -> 1133             answer, self.gateway_client, self.target_id, self.name)
>        1134 
>        1135         for temp_arg in temp_args:
>     /Users/<>/spark-2.1.0-bin-hadoop2.7/python/pyspark/sql/utils.pyc in deco(*a,
**kw)
>          61     def deco(*a, **kw):
>          62         try:
>     ---> 63             return f(*a, **kw)
>          64         except py4j.protocol.Py4JJavaError as e:
>          65             s = e.java_exception.toString()
>     /Users/<>/.virtualenvs/<>/lib/python2.7/site-packages/py4j/protocol.pyc
in get_return_value(answer, gateway_client, target_id, name)
>         317                 raise Py4JJavaError(
>         318                     "An error occurred while calling {0}{1}{2}.\n".
>     --> 319                     format(target_id, ".", name), value)
>         320             else:
>         321                 raise Py4JError(
>     Py4JJavaError: An error occurred while calling o91.showString.
>     : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage
36.0 failed 1 times, most recent failure: Lost task 0.0 in stage 36.0 (TID 645, localhost,
executor driver): org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding
attribute, tree: AmtPaidCumSum#10
> {noformat}    	
> I wonder if someone could reproduce this behaviour ...
> here is complete log ..
> {noformat}
>     Py4JJavaErrorTraceback (most recent call last)
>     <ipython-input-69-3106d06b6e49> in <module>()
>     ----> 1 df.show()
>     /Users/<>/spark-2.1.0-bin-hadoop2.7/python/pyspark/sql/dataframe.pyc in show(self,
n, truncate)
>         316         """
>         317         if isinstance(truncate, bool) and truncate:
>     --> 318             print(self._jdf.showString(n, 20))
>         319         else:
>         320             print(self._jdf.showString(n, int(truncate)))
>     /Users/<>/.virtualenvs/<>/lib/python2.7/site-packages/py4j/java_gateway.pyc
in __call__(self, *args)
>        1131         answer = self.gateway_client.send_command(command)
>        1132         return_value = get_return_value(
>     -> 1133             answer, self.gateway_client, self.target_id, self.name)
>        1134
>        1135         for temp_arg in temp_args:
>     /Users/<>/spark-2.1.0-bin-hadoop2.7/python/pyspark/sql/utils.pyc in deco(*a,
**kw)
>          61     def deco(*a, **kw):
>          62         try:
>     ---> 63             return f(*a, **kw)
>          64         except py4j.protocol.Py4JJavaError as e:
>          65             s = e.java_exception.toString()
>     /Users/<>/.virtualenvs/<>/lib/python2.7/site-packages/py4j/protocol.pyc
in get_return_value(answer, gateway_client, target_id, name)
>         317                 raise Py4JJavaError(
>         318                     "An error occurred while calling {0}{1}{2}.\n".
>     --> 319                     format(target_id, ".", name), value)
>         320             else:
>         321                 raise Py4JError(
>     Py4JJavaError: An error occurred while calling o703.showString.
>     : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage
119.0 failed 1 times, most recent failure: Lost task 0.0 in stage 119.0 (TID 1817, localhost,
executor driver): org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding
attribute, tree: AmtPaidCumSum#2076
>     	at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56)
>     	at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:88)
>     	at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:87)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:288)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:288)
>     	at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:287)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:293)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:293)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5$$anonfun$apply$11.apply(TreeNode.scala:360)
>     	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>     	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>     	at scala.collection.immutable.List.foreach(List.scala:381)
>     	at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
>     	at scala.collection.immutable.List.map(List.scala:285)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:358)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:329)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:293)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:277)
>     	at org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:87)
>     	at org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection$$anonfun$bind$1.apply(GenerateMutableProjection.scala:38)
>     	at org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection$$anonfun$bind$1.apply(GenerateMutableProjection.scala:38)
>     	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>     	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>     	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>     	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
>     	at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
>     	at scala.collection.AbstractTraversable.map(Traversable.scala:104)
>     	at org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection$.bind(GenerateMutableProjection.scala:38)
>     	at org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection$.generate(GenerateMutableProjection.scala:44)
>     	at org.apache.spark.sql.execution.SparkPlan.newMutableProjection(SparkPlan.scala:353)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$windowFrameExpressionFactoryPairs$2$$anonfun$org$apache$spark$sql$execution$window$WindowExec$$anonfun$$processor$1$1.apply(WindowExec.scala:203)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$windowFrameExpressionFactoryPairs$2$$anonfun$org$apache$spark$sql$execution$window$WindowExec$$anonfun$$processor$1$1.apply(WindowExec.scala:202)
>     	at org.apache.spark.sql.execution.window.AggregateProcessor$.apply(AggregateProcessor.scala:98)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$windowFrameExpressionFactoryPairs$2.org$apache$spark$sql$execution$window$WindowExec$$anonfun$$processor$1(WindowExec.scala:198)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$windowFrameExpressionFactoryPairs$2$$anonfun$6.apply(WindowExec.scala:225)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$windowFrameExpressionFactoryPairs$2$$anonfun$6.apply(WindowExec.scala:222)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$14$$anon$1$$anonfun$16.apply(WindowExec.scala:318)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$14$$anon$1$$anonfun$16.apply(WindowExec.scala:318)
>     	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>     	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>     	at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
>     	at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
>     	at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
>     	at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$14$$anon$1.<init>(WindowExec.scala:318)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$14.apply(WindowExec.scala:290)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$14.apply(WindowExec.scala:289)
>     	at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:796)
>     	at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:796)
>     	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>     	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
>     	at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
>     	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>     	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
>     	at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
>     	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
>     	at org.apache.spark.scheduler.Task.run(Task.scala:99)
>     	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
>     	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>     	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>     	at java.lang.Thread.run(Thread.java:745)
>     Caused by: java.lang.RuntimeException: Couldn't find AmtPaidCumSum#2076 in [sum#2299,max#2300,x#2066L,AmtPaid#2067]
>     	at scala.sys.package$.error(package.scala:27)
>     	at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:94)
>     	at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:88)
>     	at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:52)
>     	... 62 more
>     Driver stacktrace:
>     	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)
>     	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)
>     	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)
>     	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>     	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
>     	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)
>     	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
>     	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
>     	at scala.Option.foreach(Option.scala:257)
>     	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)
>     	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)
>     	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)
>     	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)
>     	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
>     	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)
>     	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1918)
>     	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1931)
>     	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1944)
>     	at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:333)
>     	at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
>     	at org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$execute$1$1.apply(Dataset.scala:2371)
>     	at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
>     	at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2765)
>     	at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$execute$1(Dataset.scala:2370)
>     	at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collect(Dataset.scala:2377)
>     	at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2113)
>     	at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2112)
>     	at org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2795)
>     	at org.apache.spark.sql.Dataset.head(Dataset.scala:2112)
>     	at org.apache.spark.sql.Dataset.take(Dataset.scala:2327)
>     	at org.apache.spark.sql.Dataset.showString(Dataset.scala:248)
>     	at sun.reflect.GeneratedMethodAccessor83.invoke(Unknown Source)
>     	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
>     	at java.lang.reflect.Method.invoke(Method.java:498)
>     	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
>     	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
>     	at py4j.Gateway.invoke(Gateway.java:280)
>     	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
>     	at py4j.commands.CallCommand.execute(CallCommand.java:79)
>     	at py4j.GatewayConnection.run(GatewayConnection.java:214)
>     	at java.lang.Thread.run(Thread.java:745)
>     Caused by: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding
attribute, tree: null
>     	at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56)
>     	at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:88)
>     	at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:87)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:288)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:288)
>     	at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:287)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:293)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:293)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5$$anonfun$apply$11.apply(TreeNode.scala:360)
>     	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>     	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>     	at scala.collection.immutable.List.foreach(List.scala:381)
>     	at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
>     	at scala.collection.immutable.List.map(List.scala:285)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:358)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:329)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:293)
>     	at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:277)
>     	at org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:87)
>     	at org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection$$anonfun$bind$1.apply(GenerateMutableProjection.scala:38)
>     	at org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection$$anonfun$bind$1.apply(GenerateMutableProjection.scala:38)
>     	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>     	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>     	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>     	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
>     	at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
>     	at scala.collection.AbstractTraversable.map(Traversable.scala:104)
>     	at org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection$.bind(GenerateMutableProjection.scala:38)
>     	at org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection$.generate(GenerateMutableProjection.scala:44)
>     	at org.apache.spark.sql.execution.SparkPlan.newMutableProjection(SparkPlan.scala:353)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$windowFrameExpressionFactoryPairs$2$$anonfun$org$apache$spark$sql$execution$window$WindowExec$$anonfun$$processor$1$1.apply(WindowExec.scala:203)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$windowFrameExpressionFactoryPairs$2$$anonfun$org$apache$spark$sql$execution$window$WindowExec$$anonfun$$processor$1$1.apply(WindowExec.scala:202)
>     	at org.apache.spark.sql.execution.window.AggregateProcessor$.apply(AggregateProcessor.scala:98)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$windowFrameExpressionFactoryPairs$2.org$apache$spark$sql$execution$window$WindowExec$$anonfun$$processor$1(WindowExec.scala:198)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$windowFrameExpressionFactoryPairs$2$$anonfun$6.apply(WindowExec.scala:225)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$windowFrameExpressionFactoryPairs$2$$anonfun$6.apply(WindowExec.scala:222)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$14$$anon$1$$anonfun$16.apply(WindowExec.scala:318)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$14$$anon$1$$anonfun$16.apply(WindowExec.scala:318)
>     	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>     	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>     	at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
>     	at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
>     	at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
>     	at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$14$$anon$1.<init>(WindowExec.scala:318)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$14.apply(WindowExec.scala:290)
>     	at org.apache.spark.sql.execution.window.WindowExec$$anonfun$14.apply(WindowExec.scala:289)
>     	at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:796)
>     	at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:796)
>     	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>     	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
>     	at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
>     	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>     	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
>     	at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
>     	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
>     	at org.apache.spark.scheduler.Task.run(Task.scala:99)
>     	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
>     	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>     	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>     	... 1 more
>     Caused by: java.lang.RuntimeException: Couldn't find AmtPaidCumSum#2076 in [sum#2299,max#2300,x#2066L,AmtPaid#2067]
>     	at scala.sys.package$.error(package.scala:27)
>     	at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:94)
>     	at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:88)
>     	at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:52)
>     	... 62 more
> {noformat}



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