Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/18460#discussion_r127160057
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala
---
@@ -144,6 +145,16 @@ object TypeCoercion {
.orElse((t1, t2) match {
case (ArrayType(et1, containsNull1), ArrayType(et2,
containsNull2)) =>
findWiderTypeForTwo(et1, et2).map(ArrayType(_, containsNull1 ||
containsNull2))
+ case (st1 @ StructType(fields1), st2 @ StructType(fields2)) if
st1.sameType(st2) =>
--- End diff --
I don't think we should put this logic here, we are not finding a "wider"
type here. I think a more proper place is in `findTightestCommonType` when we
deal with data type equality. More generally, I think we should revisit when we
should use `==` and when use `DataType.sameType` for data type equality check.
For example, we can eliminate struct type cast if unnecessary.
In this PR, even you fixed the struct type equality in type coercion, the
`Cast` doesn't know it and we will end up executing unnecessary casts.
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