[jira] [Assigned] (SPARK-36673) Incorrect Unions of struct with mismatched field name case
[ 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 > org.apache.s
[jira] [Assigned] (SPARK-36673) Incorrect Unions of struct with mismatched field name case
[ 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 > org.apache.spark.sql.catalyst.tre
[jira] [Assigned] (SPARK-36673) Incorrect Unions of struct with mismatched field name case
[ 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 > org.apac