[ 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<INNER: > bigint>] > val df2 = spark.range(2).withColumn("nested", struct(expr("id * 5 AS inner"))) > df2: org.apache.spark.sql.DataFrame = [id: bigint, nested: struct<inner: > 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$$super$transformDown(LogicalPlan.scala:29) > at > org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDown(AnalysisHelper.scala:171) > at > org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDown$(AnalysisHelper.scala:169) > at > org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDown(LogicalPlan.scala:29) > at > org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDown(LogicalPlan.scala:29) > at > org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$3(TreeNode.scala:321) > at > org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$mapChildren$1(TreeNode.scala:406) > at > org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:242) > at > org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:404) > at > org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:357) > at > org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:321) > at > org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$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$3(TreeNode.scala:321) > at > org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$mapChildren$1(TreeNode.scala:406) > at > org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:242) > at > org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:404) > at > org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:357) > at > org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:321) > at > org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$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.transform(TreeNode.scala:305) > at > org.apache.spark.sql.catalyst.optimizer.ColumnPruning$.apply(Optimizer.scala:695) > at > org.apache.spark.sql.catalyst.optimizer.ColumnPruning$.apply(Optimizer.scala:693) > at > org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:215) > at > scala.collection.LinearSeqOptimized.foldLeft(LinearSeqOptimized.scala:126) > at > scala.collection.LinearSeqOptimized.foldLeft$(LinearSeqOptimized.scala:122) > at scala.collection.immutable.List.foldLeft(List.scala:89) > at > org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:212) > at > org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:204) > at scala.collection.immutable.List.foreach(List.scala:392) > at > org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:204) > at > org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:182) > at > org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:88) > at > org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:182) > at > org.apache.spark.sql.execution.QueryExecution.$anonfun$optimizedPlan$1(QueryExecution.scala:88) > at > org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111) > at > org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:144) > at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:771) > at > org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:144) > at > org.apache.spark.sql.execution.QueryExecution.optimizedPlan$lzycompute(QueryExecution.scala:85) > at > org.apache.spark.sql.execution.QueryExecution.optimizedPlan(QueryExecution.scala:85) > at > org.apache.spark.sql.execution.QueryExecution.assertOptimized(QueryExecution.scala:96) > at > org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:114) > at > org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:111) > at > org.apache.spark.sql.execution.QueryExecution.$anonfun$simpleString$2(QueryExecution.scala:162) > at > org.apache.spark.sql.execution.ExplainUtils$.processPlan(ExplainUtils.scala:115) > at > org.apache.spark.sql.execution.QueryExecution.simpleString(QueryExecution.scala:162) > at > org.apache.spark.sql.execution.QueryExecution.org$apache$spark$sql$execution$QueryExecution$$explainString(QueryExecution.scala:207) > at > org.apache.spark.sql.execution.QueryExecution.explainString(QueryExecution.scala:176) > at > org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:98) > at > org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163) > at > org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90) > at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:771) > at > org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64) > at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3703) > at org.apache.spark.sql.Dataset.head(Dataset.scala:2740) > at org.apache.spark.sql.Dataset.take(Dataset.scala:2947) > at org.apache.spark.sql.Dataset.getRows(Dataset.scala:301) > at org.apache.spark.sql.Dataset.showString(Dataset.scala:340) > at org.apache.spark.sql.Dataset.show(Dataset.scala:827) > at org.apache.spark.sql.Dataset.show(Dataset.scala:786) > at org.apache.spark.sql.Dataset.show(Dataset.scala:795) > ... 47 elided > {code} > This behaviour was introduced in SPARK-26812. -- This message was sent by Atlassian Jira (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org