cloud-fan commented on a change in pull request #34038:
URL: https://github.com/apache/spark/pull/34038#discussion_r714485017
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
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@@ -401,15 +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]
+ val dataTypesAreCompatibleFn = if (isUnion) {
+ // `TypeCoercion` takes care of type coercion already. If any
columns or nested
+ // columns are not compatible, we detect it here and throw
analysis exception.
+ val typeChecker = (dt1: DataType, dt2: DataType) => {
+ !TypeCoercion.findWiderTypeForTwo(dt1.asNullable,
dt2.asNullable).isEmpty
Review comment:
For compatible types, spark will add implicit cast and the types should
be equal. So I think a simple `dt1 == dt2` should be sufficient here?
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