sunchao commented on a change in pull request #32764:
URL: https://github.com/apache/spark/pull/32764#discussion_r645169328
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
##########
@@ -2169,12 +2169,29 @@ class Analyzer(override val catalogManager:
CatalogManager)
unbound, arguments, unsupported)
}
+ if (bound.inputTypes().length != arguments.length) {
+ throw
QueryCompilationErrors.v2FunctionInvalidInputTypeLengthError(
+ bound, arguments)
+ }
+
+ val castedArguments = arguments.zip(bound.inputTypes()).map
{ case (arg, ty) =>
+ if (arg.dataType != ty) {
+ if (Cast.canCast(arg.dataType, ty)) {
+ Cast(arg, ty)
+ } else {
+ throw
QueryCompilationErrors.v2FunctionCastError(bound, arg, ty)
+ }
+ } else {
+ arg
+ }
+ }
Review comment:
Yes `bind` checks input types, and allows multiple combinations of them.
However, when evaluating the UDF, especially when dealing with magic method,
Spark only accept a single set of input parameter types, and so we'll need to
insert cast if necessary.
For instance, a UDF can accept both `int` and `decimal` as input types in
`bind`, and implements magic method using `decimal` parameters. Spark then
should cast `int` arguments to `decimal` when necessary.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
