[jira] [Assigned] (SPARK-36673) Incorrect Unions of struct with mismatched field name case

[Wenchen Fan (Jira)]

 [ 
https://issues.apache.org/jira/browse/SPARK-36673?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Wenchen Fan reassigned SPARK-36673:
---

Assignee: L. C. Hsieh

> Incorrect Unions of struct with mismatched field name case
> --
>
> Key: SPARK-36673
> URL: https://issues.apache.org/jira/browse/SPARK-36673
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 3.1.1, 3.2.0
>Reporter: Shardul Mahadik
>Assignee: L. C. Hsieh
>Priority: Major
>
> If a nested field has different casing on two sides of the union, the 
> resultant schema of the union will both fields in its schemaa
> {code:java}
> scala> val df1 = spark.range(2).withColumn("nested", struct(expr("id * 5 AS 
> INNER")))
> df1: org.apache.spark.sql.DataFrame = [id: bigint, nested: struct bigint>]
> val df2 = spark.range(2).withColumn("nested", struct(expr("id * 5 AS inner")))
> df2: org.apache.spark.sql.DataFrame = [id: bigint, nested: struct bigint>]
> scala> df1.union(df2).printSchema
> root
>  |-- id: long (nullable = false)
>  |-- nested: struct (nullable = false)
>  ||-- INNER: long (nullable = false)
>  ||-- inner: long (nullable = false)
>  {code}
> This seems like a bug. I would expect that Spark SQL would either just union 
> by index or if the user has requested {{unionByName}}, then it should matched 
> fields case insensitively if {{spark.sql.caseSensitive}} is {{false}}.
> However the output data only has one nested column
> {code:java}
> scala> df1.union(df2).show()
> +---+--+
> | id|nested|
> +---+--+
> |  0|   {0}|
> |  1|   {5}|
> |  0|   {0}|
> |  1|   {5}|
> +---+--+
> {code}
> Trying to project fields of {{nested}} throws an error:
> {code:java}
> scala> df1.union(df2).select("nested.*").show()
> java.lang.ArrayIndexOutOfBoundsException: 1
>   at org.apache.spark.sql.types.StructType.apply(StructType.scala:414)
>   at 
> org.apache.spark.sql.catalyst.expressions.GetStructField.dataType(complexTypeExtractors.scala:108)
>   at 
> org.apache.spark.sql.catalyst.expressions.Alias.toAttribute(namedExpressions.scala:192)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.Project.$anonfun$output$1(basicLogicalOperators.scala:63)
>   at 
> scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
>   at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
>   at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
>   at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
>   at scala.collection.TraversableLike.map(TraversableLike.scala:238)
>   at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
>   at scala.collection.AbstractTraversable.map(Traversable.scala:108)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.Project.output(basicLogicalOperators.scala:63)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.Union.$anonfun$output$3(basicLogicalOperators.scala:260)
>   at 
> scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
>   at scala.collection.immutable.List.foreach(List.scala:392)
>   at scala.collection.TraversableLike.map(TraversableLike.scala:238)
>   at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
>   at scala.collection.immutable.List.map(List.scala:298)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.Union.output(basicLogicalOperators.scala:260)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.outputSet$lzycompute(QueryPlan.scala:49)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.outputSet(QueryPlan.scala:49)
>   at 
> org.apache.spark.sql.catalyst.optimizer.ColumnPruning$$anonfun$apply$8.applyOrElse(Optimizer.scala:747)
>   at 
> org.apache.spark.sql.catalyst.optimizer.ColumnPruning$$anonfun$apply$8.applyOrElse(Optimizer.scala:695)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$1(TreeNode.scala:316)
>   at 
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:72)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:316)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDown(LogicalPlan.scala:29)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDown(AnalysisHelper.scala:171)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDown$(AnalysisHelper.scala:169)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDown(LogicalPlan.scala:29)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDown(LogicalPlan.scala:29)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$3(TreeNode.scala:321)
>   at 
> 

[jira] [Assigned] (SPARK-36673) Incorrect Unions of struct with mismatched field name case

[Apache Spark (Jira)]

 [ 
https://issues.apache.org/jira/browse/SPARK-36673?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Apache Spark reassigned SPARK-36673:


Assignee: (was: Apache Spark)

> Incorrect Unions of struct with mismatched field name case
> --
>
> Key: SPARK-36673
> URL: https://issues.apache.org/jira/browse/SPARK-36673
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 3.1.1, 3.2.0
>Reporter: Shardul Mahadik
>Priority: Major
>
> If a nested field has different casing on two sides of the union, the 
> resultant schema of the union will both fields in its schemaa
> {code:java}
> scala> val df1 = spark.range(2).withColumn("nested", struct(expr("id * 5 AS 
> INNER")))
> df1: org.apache.spark.sql.DataFrame = [id: bigint, nested: struct bigint>]
> val df2 = spark.range(2).withColumn("nested", struct(expr("id * 5 AS inner")))
> df2: org.apache.spark.sql.DataFrame = [id: bigint, nested: struct bigint>]
> scala> df1.union(df2).printSchema
> root
>  |-- id: long (nullable = false)
>  |-- nested: struct (nullable = false)
>  ||-- INNER: long (nullable = false)
>  ||-- inner: long (nullable = false)
>  {code}
> This seems like a bug. I would expect that Spark SQL would either just union 
> by index or if the user has requested {{unionByName}}, then it should matched 
> fields case insensitively if {{spark.sql.caseSensitive}} is {{false}}.
> However the output data only has one nested column
> {code:java}
> scala> df1.union(df2).show()
> +---+--+
> | id|nested|
> +---+--+
> |  0|   {0}|
> |  1|   {5}|
> |  0|   {0}|
> |  1|   {5}|
> +---+--+
> {code}
> Trying to project fields of {{nested}} throws an error:
> {code:java}
> scala> df1.union(df2).select("nested.*").show()
> java.lang.ArrayIndexOutOfBoundsException: 1
>   at org.apache.spark.sql.types.StructType.apply(StructType.scala:414)
>   at 
> org.apache.spark.sql.catalyst.expressions.GetStructField.dataType(complexTypeExtractors.scala:108)
>   at 
> org.apache.spark.sql.catalyst.expressions.Alias.toAttribute(namedExpressions.scala:192)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.Project.$anonfun$output$1(basicLogicalOperators.scala:63)
>   at 
> scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
>   at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
>   at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
>   at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
>   at scala.collection.TraversableLike.map(TraversableLike.scala:238)
>   at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
>   at scala.collection.AbstractTraversable.map(Traversable.scala:108)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.Project.output(basicLogicalOperators.scala:63)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.Union.$anonfun$output$3(basicLogicalOperators.scala:260)
>   at 
> scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
>   at scala.collection.immutable.List.foreach(List.scala:392)
>   at scala.collection.TraversableLike.map(TraversableLike.scala:238)
>   at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
>   at scala.collection.immutable.List.map(List.scala:298)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.Union.output(basicLogicalOperators.scala:260)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.outputSet$lzycompute(QueryPlan.scala:49)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.outputSet(QueryPlan.scala:49)
>   at 
> org.apache.spark.sql.catalyst.optimizer.ColumnPruning$$anonfun$apply$8.applyOrElse(Optimizer.scala:747)
>   at 
> org.apache.spark.sql.catalyst.optimizer.ColumnPruning$$anonfun$apply$8.applyOrElse(Optimizer.scala:695)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$1(TreeNode.scala:316)
>   at 
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:72)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:316)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDown(LogicalPlan.scala:29)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDown(AnalysisHelper.scala:171)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDown$(AnalysisHelper.scala:169)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDown(LogicalPlan.scala:29)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDown(LogicalPlan.scala:29)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$3(TreeNode.scala:321)
>   at 
> 

[jira] [Assigned] (SPARK-36673) Incorrect Unions of struct with mismatched field name case

[Apache Spark (Jira)]

 [ 
https://issues.apache.org/jira/browse/SPARK-36673?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Apache Spark reassigned SPARK-36673:


Assignee: Apache Spark

> Incorrect Unions of struct with mismatched field name case
> --
>
> Key: SPARK-36673
> URL: https://issues.apache.org/jira/browse/SPARK-36673
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 3.1.1, 3.2.0
>Reporter: Shardul Mahadik
>Assignee: Apache Spark
>Priority: Major
>
> If a nested field has different casing on two sides of the union, the 
> resultant schema of the union will both fields in its schemaa
> {code:java}
> scala> val df1 = spark.range(2).withColumn("nested", struct(expr("id * 5 AS 
> INNER")))
> df1: org.apache.spark.sql.DataFrame = [id: bigint, nested: struct bigint>]
> val df2 = spark.range(2).withColumn("nested", struct(expr("id * 5 AS inner")))
> df2: org.apache.spark.sql.DataFrame = [id: bigint, nested: struct bigint>]
> scala> df1.union(df2).printSchema
> root
>  |-- id: long (nullable = false)
>  |-- nested: struct (nullable = false)
>  ||-- INNER: long (nullable = false)
>  ||-- inner: long (nullable = false)
>  {code}
> This seems like a bug. I would expect that Spark SQL would either just union 
> by index or if the user has requested {{unionByName}}, then it should matched 
> fields case insensitively if {{spark.sql.caseSensitive}} is {{false}}.
> However the output data only has one nested column
> {code:java}
> scala> df1.union(df2).show()
> +---+--+
> | id|nested|
> +---+--+
> |  0|   {0}|
> |  1|   {5}|
> |  0|   {0}|
> |  1|   {5}|
> +---+--+
> {code}
> Trying to project fields of {{nested}} throws an error:
> {code:java}
> scala> df1.union(df2).select("nested.*").show()
> java.lang.ArrayIndexOutOfBoundsException: 1
>   at org.apache.spark.sql.types.StructType.apply(StructType.scala:414)
>   at 
> org.apache.spark.sql.catalyst.expressions.GetStructField.dataType(complexTypeExtractors.scala:108)
>   at 
> org.apache.spark.sql.catalyst.expressions.Alias.toAttribute(namedExpressions.scala:192)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.Project.$anonfun$output$1(basicLogicalOperators.scala:63)
>   at 
> scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
>   at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
>   at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
>   at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
>   at scala.collection.TraversableLike.map(TraversableLike.scala:238)
>   at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
>   at scala.collection.AbstractTraversable.map(Traversable.scala:108)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.Project.output(basicLogicalOperators.scala:63)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.Union.$anonfun$output$3(basicLogicalOperators.scala:260)
>   at 
> scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
>   at scala.collection.immutable.List.foreach(List.scala:392)
>   at scala.collection.TraversableLike.map(TraversableLike.scala:238)
>   at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
>   at scala.collection.immutable.List.map(List.scala:298)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.Union.output(basicLogicalOperators.scala:260)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.outputSet$lzycompute(QueryPlan.scala:49)
>   at 
> org.apache.spark.sql.catalyst.plans.QueryPlan.outputSet(QueryPlan.scala:49)
>   at 
> org.apache.spark.sql.catalyst.optimizer.ColumnPruning$$anonfun$apply$8.applyOrElse(Optimizer.scala:747)
>   at 
> org.apache.spark.sql.catalyst.optimizer.ColumnPruning$$anonfun$apply$8.applyOrElse(Optimizer.scala:695)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$1(TreeNode.scala:316)
>   at 
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:72)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:316)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDown(LogicalPlan.scala:29)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDown(AnalysisHelper.scala:171)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDown$(AnalysisHelper.scala:169)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDown(LogicalPlan.scala:29)
>   at 
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDown(LogicalPlan.scala:29)
>   at 
> org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$3(TreeNode.scala:321)
>   at 
>