[GitHub] [spark] viirya commented on a change in pull request #34038: [SPARK-36797][SQL] Union should resolve nested columns as top-level columns

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viirya commented on a change in pull request #34038:
URL: https://github.com/apache/spark/pull/34038#discussion_r711537729



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File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
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@@ -401,16 +401,30 @@ trait CheckAnalysis extends PredicateHelper with LookupCatalog {
                     |the ${ordinalNumber(ti + 1)} table has ${child.output.length} columns
                   """.stripMargin.replace("\n", " ").trim())
               }
+              val isUnion = operator.isInstanceOf[Union]
               // Check if the data types match.
-              dataTypes(child).zip(ref).zipWithIndex.foreach { case ((dt1, dt2), ci) =>
-                // SPARK-18058: we shall not care about the nullability of columns
-                if (TypeCoercion.findWiderTypeForTwo(dt1.asNullable, dt2.asNullable).isEmpty) {
-                  failAnalysis(
-                    s"""
-                      |${operator.nodeName} can only be performed on tables with the compatible
-                      |column types. ${dt1.catalogString} <> ${dt2.catalogString} at the
-                      |${ordinalNumber(ci)} column of the ${ordinalNumber(ti + 1)} table
-                    """.stripMargin.replace("\n", " ").trim())
+              if (!isUnion) {

Review comment:
       Not sure if we should also generalize to all set operations? Although it looks reasonable, but by their API definition seems we don't have the by-position definition as Union.




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