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https://issues.apache.org/jira/browse/SPARK-21966?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16160877#comment-16160877
]
Feng Zhu commented on SPARK-21966:
----------------------------------
The rule ResolveMissingReference does not plan to support binary nodes.
This rule could be very complex for a complete support.
> ResolveMissingReference rule should not ignore the Union operator
> -----------------------------------------------------------------
>
> Key: SPARK-21966
> URL: https://issues.apache.org/jira/browse/SPARK-21966
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.1.0, 2.1.1, 2.2.0
> Reporter: Feng Zhu
>
> The below example will fail.
> {code:java}
> val df1 = spark.createDataFrame(Seq((1, 1), (2, 1), (2, 2))).toDF("a", "b")
> val df2 = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 3))).toDF("a", "b")
> val df3 = df1.cube("a").sum("b")
> val df4 = df2.cube("a").sum("b")
> val df5 = df3.union(df4).filter("grouping_id()=0").show()
> {code}
> It will thow an Exception:
> {code:java}
> Exception in thread "main" org.apache.spark.sql.AnalysisException: cannot
> resolve '`spark_grouping_id`' given input columns: [a, sum(b)];;
> 'Filter ('spark_grouping_id > 0)
> +- Union
> :- Aggregate [a#17, spark_grouping_id#15], [a#17, sum(cast(b#6 as bigint))
> AS sum(b)#14L]
> : +- Expand [List(a#5, b#6, a#16, 0), List(a#5, b#6, null, 1)], [a#5,
> b#6, a#17, spark_grouping_id#15]
> : +- Project [a#5, b#6, a#5 AS a#16]
> : +- Project [_1#0 AS a#5, _2#1 AS b#6]
> : +- LocalRelation [_1#0, _2#1]
> +- Aggregate [a#30, spark_grouping_id#28], [a#30, sum(cast(b#6 as bigint))
> AS sum(b)#27L]
> +- Expand [List(a#5, b#6, a#29, 0), List(a#5, b#6, null, 1)], [a#5,
> b#6, a#30, spark_grouping_id#28]
> +- Project [a#5, b#6, a#5 AS a#29]
> +- Project [_1#0 AS a#5, _2#1 AS b#6]
> +- LocalRelation [_1#0, _2#1]
> at
> org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:77)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:310)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:310)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:309)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:307)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:307)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:331)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:329)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:307)
> at
> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:282)
> at
> org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:292)
> at
> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$7.apply(QueryPlan.scala:301)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
> at
> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:301)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:74)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:128)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:57)
> at
> org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:48)
> at
> org.apache.spark.sql.execution.QueryExecution.withCachedData$lzycompute(QueryExecution.scala:72)
> at
> org.apache.spark.sql.execution.QueryExecution.withCachedData(QueryExecution.scala:71)
> at
> org.apache.spark.sql.execution.QueryExecution.optimizedPlan$lzycompute(QueryExecution.scala:77)
> at
> org.apache.spark.sql.execution.QueryExecution.optimizedPlan(QueryExecution.scala:77)
> at
> org.apache.spark.sql.execution.QueryExecution.<init>(QueryExecution.scala:79)
> at
> org.apache.spark.sql.internal.SessionState.executePlan(SessionState.scala:169)
> at org.apache.spark.sql.Dataset.<init>(Dataset.scala:167)
> at org.apache.spark.sql.Dataset$.apply(Dataset.scala:58)
> at org.apache.spark.sql.Dataset.withTypedPlan(Dataset.scala:2827)
> at org.apache.spark.sql.Dataset.filter(Dataset.scala:1272)
> at org.apache.spark.sql.Dataset.filter(Dataset.scala:1286)
> at SparkSQLExample$.main(SparkSQLExample.scala:57)
> {code}
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