Github user chenghao-intel commented on a diff in the pull request:
https://github.com/apache/spark/pull/7151#discussion_r33683599
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
---
@@ -705,13 +705,13 @@ object HiveTypeCoercion {
* Casts types according to the expected input types for Expressions
that have the trait
* [[AutoCastInputTypes]].
*/
- object AddCastForAutoCastInputTypes extends Rule[LogicalPlan] {
+ object ImplicitTypeCasts extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan
transformAllExpressions {
// Skip nodes who's children have not been resolved yet.
case e if !e.childrenResolved => e
- case e: AutoCastInputTypes if e.children.map(_.dataType) !=
e.expectedChildTypes =>
- val newC = (e.children, e.children.map(_.dataType),
e.expectedChildTypes).zipped.map {
+ case e: AutoCastInputTypes if e.children.map(_.dataType) !=
e.inputTypes =>
+ val newC = (e.children, e.children.map(_.dataType),
e.inputTypes).zipped.map {
case (child, actual, expected) =>
if (actual == expected) child else Cast(child, expected)
--- End diff --
The original logic probably a bug.
Let's say:
The expression "printf(format, ...)", we expect the first parameter to be
`StringType`, thus any other type (e.g. `IntegerType`) can be casted to
`StringType`, but doesn't mean we have to accept the casting for its child.
The same for the other string expression like `Subtring(str, pos, len)`,
predicates, misc etc.
Actually I doubt we have the real case to accept the auto casting, except
the mathmatic expressions.
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