[SPARK-36672] Using value from map in grouping sets result org.apache.spark.sql.AnalysisException

• Type: Bug
• Status: Resolved
• Priority: Major
• Resolution: Duplicate
• Affects Version/s: 3.1.0
• Component/s: SQL
• Assignee: Unassigned
• Reporter: Jack Hu
• Created: 06/Sep/21 05:50
• Updated: 07/Sep/21 04:57
• Resolved: 07/Sep/21 04:57

Description

Steps to reproduce：

1. create a table with map

   create table test (int_value INT, dims MAP<string, string>) using parquet

2. Run following query:

   select int_value, count(1)
   from test
   group by int_value, dims.dim_x, dims.dim_y
   grouping sets ( (int_value, dims.dim_x), (int_value, dims.dim_y))

The call stack:

org.apache.spark.sql.AnalysisException: dims#34[dim_x] AS dim_x#35 doesn't show up in the GROUP BY list ArrayBuffer(int_value#33 AS int_value#41, dims#34[dim_x] AS dim_x#37 AS dim_x#42, dims#34[dim_y] AS dim_y#38 AS dim_y#43);
	at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:41)
	at org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:92)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGroupingAnalytics$$anonfun$19$$anonfun$apply$49$$anonfun$21.apply(Analyzer.scala:387)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGroupingAnalytics$$anonfun$19$$anonfun$apply$49$$anonfun$21.apply(Analyzer.scala:387)
	at scala.Option.getOrElse(Option.scala:121)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGroupingAnalytics$$anonfun$19$$anonfun$apply$49.apply(Analyzer.scala:386)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGroupingAnalytics$$anonfun$19$$anonfun$apply$49.apply(Analyzer.scala:385)
	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$class.map(TraversableLike.scala:234)
	at scala.collection.AbstractTraversable.map(Traversable.scala:104)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGroupingAnalytics$$anonfun$19.apply(Analyzer.scala:385)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGroupingAnalytics$$anonfun$19.apply(Analyzer.scala:384)
	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$class.map(TraversableLike.scala:234)
	at scala.collection.AbstractTraversable.map(Traversable.scala:104)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGroupingAnalytics$.constructExpand(Analyzer.scala:384)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGroupingAnalytics$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveGroupingAnalytics$$constructAggregate(Analyzer.scala:448)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGroupingAnalytics$$anonfun$apply$6.applyOrElse(Analyzer.scala:485)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGroupingAnalytics$$anonfun$apply$6.applyOrElse(Analyzer.scala:473)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289)
	at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
	at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:288)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGroupingAnalytics$.apply(Analyzer.scala:473)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGroupingAnalytics$.apply(Analyzer.scala:287)
	at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:87)
	at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:84)
	at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
	at scala.collection.immutable.List.foldLeft(List.scala:84)
	at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:84)
	at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:76)
	at scala.collection.immutable.List.foreach(List.scala:381)
	at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:76)
	at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:124)
	at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:118)
	at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:103)
	at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:57)
	at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:55)
	at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:47)
	at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:74)
	at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:641)

 

Issue Links

• duplicates: SPARK-33308 support CUBE(...) and ROLLUP(...), GROUPING SETS(...) as group by expr in parser level