aokolnychyi commented on code in PR #40206:
URL: https://github.com/apache/spark/pull/40206#discussion_r1119509603
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TableOutputResolver.scala:
##########
@@ -139,33 +139,46 @@ object TableOutputResolver {
private def resolveStructType(
input: NamedExpression,
inputType: StructType,
+ expectedCol: Attribute,
expectedType: StructType,
- expectedName: String,
conf: SQLConf,
addError: String => Unit,
colPath: Seq[String]): Option[NamedExpression] = {
val fields = inputType.zipWithIndex.map { case (f, i) =>
Alias(GetStructField(input, i, Some(f.name)), f.name)()
}
- val reordered = reorderColumnsByName(fields, expectedType.toAttributes,
conf, addError, colPath)
- if (reordered.length == expectedType.length) {
+ val fieldAttrs = createStructFieldAttrs(expectedCol, expectedType)
+ val reordered = reorderColumnsByName(fields, fieldAttrs, conf, addError,
colPath)
+ if (reordered.length == fieldAttrs.length) {
val struct = CreateStruct(reordered)
val res = if (input.nullable) {
If(IsNull(input), Literal(null, struct.dataType), struct)
} else {
struct
}
- Some(Alias(res, expectedName)())
+ Some(Alias(res, expectedCol.name)())
} else {
None
}
}
+ private def createStructFieldAttrs(attr: Attribute, attrType: StructType):
Seq[Attribute] = {
Review Comment:
I can't say I am entirely happy with this solution so if there are any
ideas, let me know.
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