daniel-lunin-db commented on code in PR #56902:
URL: https://github.com/apache/spark/pull/56902#discussion_r3507120235
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/SQLFunction.scala:
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@@ -299,6 +300,27 @@ object SQLFunction {
parser.parseExpression(text)
}
+ /**
+ * Pads `arguments` with the trailing parameters' declared defaults so the
result aligns
+ * one-to-one with `param`'s fields, parsing each default and casting it to
the parameter type.
+ * A missing parameter without a default raises
[[QueryCompilationErrors.wrongNumArgsError]].
+ */
+ def padArgumentsWithDefaults(
+ arguments: Seq[Expression],
+ param: StructType,
+ functionName: String,
+ parser: ParserInterface): Seq[Expression] = {
+ arguments ++ param.takeRight(param.size - arguments.size).map { field =>
+ field.getDefault() match {
+ case Some(default) =>
+ Cast(parseDefault(default, parser), field.dataType)
Review Comment:
@MaxGekk good catch, the summary was stale from an earlier version. It's
intentionally scalar-only: a straight, byte-identical extraction of
`makeSQLFunctionPlan`'s existing logic, cast included, and
`makeSQLTableFunctionPlan` keeps its own padding.
I kept it byte-identical on purpose to keep this a pure extraction. The
padding cast seems to be redundant given the downstream `Alias(Cast(outer,
param.dataType))`, but dropping it and moving the table path onto an untyped
helper is a separate cleanup I left out of scope. Factored into `SQLFunction`
for reuse, the single-pass analyzer needs the same padding.
Fixed the description to match.
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