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https://issues.apache.org/jira/browse/SPARK-22641?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16270132#comment-16270132
]
Andrew Duffy commented on SPARK-22641:
--------------------------------------
Query plan with the literal:
{code}
== Parsed Logical Plan ==
'Project [a#98, b#102, ident('b) AS fails_here#106]
+- Project [a#98, qq AS b#102]
+- Deduplicate [a#98]
+- LogicalRDD [a#98], false
== Analyzed Logical Plan ==
a: string, b: string, fails_here: string
Project [a#98, b#102, ident(b#102) AS fails_here#106]
+- Project [a#98, qq AS b#102]
+- Deduplicate [a#98]
+- LogicalRDD [a#98], false
== Optimized Logical Plan ==
Aggregate [a#98], [a#98, qq AS b#102, ident(qq) AS fails_here#106]
+- LogicalRDD [a#98], false
== Physical Plan ==
*HashAggregate(keys=[a#98], functions=[], output=[a#98, b#102, fails_here#106])
+- Exchange hashpartitioning(a#98, 200)
+- BatchEvalPython [ident(qq)], [a#98, pythonUDF0#111]
+- *HashAggregate(keys=[a#98], functions=[], output=[a#98])
+- Scan ExistingRDD[a#98]
{code}
And with {{F.col('a')}}
{code}
== Parsed Logical Plan ==
'Project [a#56, b#60, ident('b) AS fails_here#64]
+- Project [a#56, a#56 AS b#60]
+- Deduplicate [a#56]
+- LogicalRDD [a#56], false
== Analyzed Logical Plan ==
a: string, b: string, fails_here: string
Project [a#56, b#60, ident(b#60) AS fails_here#64]
+- Project [a#56, a#56 AS b#60]
+- Deduplicate [a#56]
+- LogicalRDD [a#56], false
== Optimized Logical Plan ==
Project [a#56, b#60, ident(a#56) AS fails_here#64]
+- Aggregate [a#56], [a#56, a#56 AS b#60, a#56]
+- LogicalRDD [a#56], false
== Physical Plan ==
*Project [a#56, b#60, pythonUDF0#69 AS fails_here#64]
+- BatchEvalPython [ident(a#56)], [a#56, b#60, pythonUDF0#69]
+- *Project [a#56, b#60]
+- *HashAggregate(keys=[a#56], functions=[], output=[a#56, b#60, a#56])
+- Exchange hashpartitioning(a#56, 200)
+- *HashAggregate(keys=[a#56], functions=[], output=[a#56])
+- Scan ExistingRDD[a#56]
{code}
> Pyspark UDF relying on column added with withColumn after distinct
> ------------------------------------------------------------------
>
> Key: SPARK-22641
> URL: https://issues.apache.org/jira/browse/SPARK-22641
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.3.0
> Reporter: Andrew Duffy
>
> We seem to have found an issue with PySpark UDFs interacting with
> {{withColumn}} when the UDF depends on the column added in {{withColumn}},
> but _only_ if {{withColumn}} is performed after a {{distinct()}}.
> Simplest repro in a local PySpark shell:
> {code}
> import pyspark.sql.functions as F
> @F.udf
> def ident(x):
> return x
> spark.createDataFrame([{'a': '1'}]) \
> .distinct() \
> .withColumn('b', F.lit('qq')) \
> .withColumn('fails_here', ident('b')) \
> .collect()
> {code}
> This fails with the following exception:
> {code}
> Py4JJavaError: An error occurred while calling o1321.collectToPython.
> : org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding
> attribute, tree: pythonUDF0#306
> at
> org.apache.spark.sql.catalyst.errors.package$.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$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
> at
> org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:87)
> at
> org.apache.spark.sql.execution.aggregate.HashAggregateExec$$anonfun$33.apply(HashAggregateExec.scala:475)
> at
> org.apache.spark.sql.execution.aggregate.HashAggregateExec$$anonfun$33.apply(HashAggregateExec.scala:474)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
> at scala.collection.AbstractTraversable.map(Traversable.scala:104)
> at
> org.apache.spark.sql.execution.aggregate.HashAggregateExec.generateResultCode(HashAggregateExec.scala:474)
> at
> org.apache.spark.sql.execution.aggregate.HashAggregateExec.doProduceWithKeys(HashAggregateExec.scala:612)
> at
> org.apache.spark.sql.execution.aggregate.HashAggregateExec.doProduce(HashAggregateExec.scala:148)
> at
> org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:85)
> at
> org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:80)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135)
> at
> org.apache.spark.sql.execution.CodegenSupport$class.produce(WholeStageCodegenExec.scala:80)
> at
> org.apache.spark.sql.execution.aggregate.HashAggregateExec.produce(HashAggregateExec.scala:38)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec.doCodeGen(WholeStageCodegenExec.scala:331)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:372)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:116)
> at
> org.apache.spark.sql.execution.SparkPlan.getByteArrayRdd(SparkPlan.scala:228)
> at
> org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:275)
> at
> org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply$mcI$sp(Dataset.scala:2872)
> at
> org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:2869)
> at
> org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:2869)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
> at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2892)
> at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:2869)
> at sun.reflect.GeneratedMethodAccessor60.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:748)
> Caused by: java.lang.RuntimeException: Couldn't find pythonUDF0#306 in [a#293]
> 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)
> ... 58 more
> {code}
> The odd part is that if you run the code, but remove the {{.distinct()}}, or
> place it after either of the {{.withColumn}} lines, we don't get the error.
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