viirya commented on a change in pull request #34038:
URL: https://github.com/apache/spark/pull/34038#discussion_r712593090
##########
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:
I think these set operations work basically the same. The by-position
resolution, I think, is to follow SQL. It only requires the columns to union
have the same data types in same order, but not column names.
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