[GitHub] [spark] cloud-fan commented on a change in pull request #35404: [SPARK-38118][SQL] Func(wrong data type) in the HAVING claus should throw data mismatch error

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cloud-fan commented on a change in pull request #35404:
URL: https://github.com/apache/spark/pull/35404#discussion_r803306323



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File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
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@@ -4249,7 +4250,30 @@ object ApplyCharTypePadding extends Rule[LogicalPlan] {
  * rule right after the main resolution batch.
  */
 object RemoveTempResolvedColumn extends Rule[LogicalPlan] {
-  override def apply(plan: LogicalPlan): LogicalPlan = plan.resolveExpressions {
-    case t: TempResolvedColumn => UnresolvedAttribute(t.nameParts)
+  override def apply(plan: LogicalPlan): LogicalPlan = {
+    plan.foreachUp {
+      // Having clause could be resolved as a Filter. When having func(column with wrong data type),
+      // the column could be wrapped by a TempResolvedColumn, e.g. mean(tempresolvedcolumn(t.c)).
+      // Because TempResolvedColumn can still preserve column data type, here is a chance to check
+      // if the data type matches with the required data type of the function. We can throw an error
+      // when data types mismatches.
+      case operator: Filter =>
+        operator.expressions.foreach(_.foreachUp {
+          case e: Expression if e.checkInputDataTypes().isFailure =>

Review comment:
       A straightforward fix is to change it to `case e: Expression if e.childrenResolved && e.checkInputDataTypes().isFailure`.
   
   A new idea is to not strip `TempResolvedColumn`, as `TempResolvedColumn` always means a failure. We can update `CheckAnalysis` to handle it, i.e. adding a new case after `case e: Expression if e.checkInputDataTypes().isFailure =>`
   ```
   case t: TempResolvedColumn =>
     val a = UnresolvedAttribute(t.nameParts)
     the same code that handles "case a: Attribute if !a.resolved"
   ```




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