Pyspark使用窗口函数时出现错误（Spark 2.1.0报告列未找到的问题）？

2024-09-28 05:23:33 发布

您现在位置：Python中文网/ 问答频道 /正文
3128 3

更新： 我创建了以下JIRA问题：https://issues.apache.org/jira/browse/SPARK-20086状态：已修复！周末的时候！太快了！）在

更新2： 此问题由https://github.com/apache/spark/pull/17432针对版本2.1.1, 2.2.0修复。所以我从http://people.apache.org/~pwendell/spark-nightly/的夜间构建中得到了一个更新的spark版本 如果您在<=2.1.0上，您可能仍然会遇到此问题。在

原文：

我在使用pyspark窗口函数时出错。下面是一些示例代码：

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()

给出：

^{pr2}$

接下来，计算累计和

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()

给予

+---+-------+-------------+
|  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|
+---+-------+-------------+

接下来，计算累计最大值

df = df.withColumn('AmtPaidCumSumMax', sf.max(sf.col('AmtPaidCumSum')).over(win_spec_max))

df.show()

给出错误日志

 Py4JJavaError: An error occurred while calling o2609.showString.

通过回溯：

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(

但有趣的是，如果我在第二个窗口操作之前引入另一个更改，比如插入一个列，则不会出现该错误：

df = df.withColumn('MaxBound', sf.lit(6.))
df.show()
+---+-------+-------------+--------+
|  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|
+---+-------+-------------+--------+


#then apply the second window operations
df = df.withColumn('AmtPaidCumSumMax', sf.max(sf.col('AmtPaidCumSum')).over(win_spec_max))
df.show()

+---+-------+-------------+--------+----------------+
|  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|
+---+-------+-------------+--------+----------------+

我不理解这种行为

好吧，到目前为止还不错，但是我尝试了另一个操作，然后又出现了类似的错误：

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()

给予

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

我想知道是否有人能重现这种行为。。。在

这是完整的日志。。在

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

Tags： orgsqlapachewindowatcollectionsparkapply
1条回答
网友
1楼 · 发布于 2024-09-28 05:23:33

Spark2.1和2.2.0-SNAPSHOT中的窗口操作符支持可能有问题（今天从master构建）。请参见Scala中的以下内容。在

你认为你应该在Spark的JIRA报告一个问题。在

val inventory = Seq(
  (1, 2.0), (1, 3.0), (1, 1.0), (1, -2.0), (1, -1.0)).toDF("x", "AmtPaid")

scala> inventory.printSchema
root
 |  x: integer (nullable = false)
 |  AmtPaid: double (nullable = false)

import org.apache.spark.sql.expressions.Window
val byXwithAllRowsBefore = Window.partitionBy("x").rowsBetween(Window.unboundedPreceding, Window.currentRow)

import org.apache.spark.sql.functions.sum
val sumOverAmtPaid = inventory.withColumn("AmtPaidCumSum", sum($"AmtPaid") over byXwithAllRowsBefore)

scala> sumOverAmtPaid.show
+ -+   -+      -+
|  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|
+ -+   -+      -+

scala> sumOverAmtPaid.printSchema
root
 |  x: integer (nullable = false)
 |  AmtPaid: double (nullable = false)
 |  AmtPaidCumSum: double (nullable = true)

到目前为止还不错。就像在Python中一样。在

累计最大值

由于java.lang.RuntimeException，以下操作将不起作用。在

^{pr2}$

RuntimeException显示：

Couldn't find AmtPaidCumSum#11 in [sum#234,max#235,x#5,AmtPaid#6]

似乎有sum列，不是吗？让我们用它代替max中的$"AmtPaidCumSum"。在

但是这次Spark报告了一个AnalysisException，其中包括AmtPaidCumSum列（！）在

org.apache.spark.sql.AnalysisException: cannot resolve 'sum' given input columns: [x, AmtPaid, AmtPaidCumSum];;

scala> val cumulativeMax = sumOverAmtPaid.withColumn("AmtPaidCumSumMax", max($"sum") over byXwithAllRowsBefore)
org.apache.spark.sql.AnalysisException: cannot resolve '`sum`' given input columns: [x, AmtPaid, AmtPaidCumSum];;
'Project [x#5, AmtPaid#6, AmtPaidCumSum#11, max('sum) windowspecdefinition(x#5, ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS AmtPaidCumSumMax#237]
+- Project [x#5, AmtPaid#6, AmtPaidCumSum#11]
   +- Project [x#5, AmtPaid#6, AmtPaidCumSum#11, AmtPaidCumSum#11]
      +- Window [sum(AmtPaid#6) windowspecdefinition(x#5, ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS AmtPaidCumSum#11], [x#5]
         +- Project [x#5, AmtPaid#6]
            +- Project [_1#2 AS x#5, _2#3 AS AmtPaid#6]
               +- LocalRelation [_1#2, _2#3]

  at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:89)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:86)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289)
  at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:288)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:286)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:286)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:286)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:286)
  at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$transformExpressionsUp$1.apply(QueryPlan.scala:256)
  at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$transformExpressionsUp$1.apply(QueryPlan.scala:256)
  at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpression$1(QueryPlan.scala:267)
  at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$1(QueryPlan.scala:277)
  at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$1$1.apply(QueryPlan.scala:281)
  at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
  at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
  at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
  at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
  at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
  at scala.collection.AbstractTraversable.map(Traversable.scala:104)
  at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$1(QueryPlan.scala:281)
  at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$6.apply(QueryPlan.scala:286)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
  at org.apache.spark.sql.catalyst.plans.QueryPlan.mapExpressions(QueryPlan.scala:286)
  at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:256)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:86)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:79)
  at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:79)
  at org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:90)
  at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:53)
  at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:67)
  at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:2832)
  at org.apache.spark.sql.Dataset.select(Dataset.scala:1137)
  at org.apache.spark.sql.Dataset.withColumn(Dataset.scala:1882)
  ... 48 elided

相关问题 更多 >

    编程相关推荐

    热门问题