metanil commented on code in PR #59760:
URL: https://github.com/apache/spark/pull/59760#discussion_r4463966108
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/TransformExpression.scala:
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@@ -202,4 +353,63 @@ object TransformExpression {
case t: TransformExpression => t.reducedWith.isDefined
case _ => false
}
+
+ /**
+ * `argument` at `declaredType`, or `None` when the function could not take
it. See
+ * [[TransformExpression.evaluableFunction]].
+ *
+ * An argument goes as it is when it is stored the way the declared type is
(`storedAlike`),
+ * which is how a function read it before this check. A numeric argument
narrower than declared
+ * (`UpCastRule.legalNumericPrecedence`), e.g. a `SMALLINT` against an `INT`
or an `INT` against a
+ * `DOUBLE`, is held as another JVM type, so it is cast. That gives the
value a magic `invoke` got
+ * from Java's widening before this check, which can lose digits for a
`BIGINT` against a
+ * `DOUBLE` as it did there, and needs no time zone. Anything else is
refused,
+ * a `DATE` against a `TIMESTAMP` in particular, whose cast takes the
session time zone.
+ */
+ private def atDeclaredType(argument: Expression, declaredType: DataType):
Option[Expression] =
+ (argument.dataType, declaredType) match {
+ case (argumentType, _) if storedAlike(argumentType, declaredType, nested
= false) =>
+ Some(argument)
+ case (argumentType, _) if
UpCastRule.legalNumericPrecedence(argumentType, declaredType) =>
Review Comment:
Agreed, both are lossless and need no time zone. Since master failed on
both, this is a missed plan rather than a regression, so I'd rather keep it out
of this PR, which is already fairly large. I'd do it as a follow-up with two
explicit arms, as you suggest, since `canUpCast` alone would also let any
`atomic` type through to `STRING`. I'll file a ticket for it.
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