viirya commented on a change in pull request #34038:
URL: https://github.com/apache/spark/pull/34038#discussion_r714490029
##########
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:
Oh, I spent a little time to recall why I keep original check logic.
It is because if `TypeCoercion` fails to find compatible types for all
columns, it won't add cast for all. It is all or nothing logic here.
So if we only check `dt1 == dt2` here, we compare the original data types
even some of them are compatible.
`AnalysisErrorSuite` has one example. One relation has `short, string,
double, decimal`, another one has `string, string, string, map`.
The first three columns are compatible, only the fourth isn't. So
`TypeCoercion` fails to add casts for all.
If we compare `dt1 == dt2`, the error will be like "short is not compatible
with string". But currently we get like "decimal is not compatible with map".
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