Github user mt40 commented on a diff in the pull request:
https://github.com/apache/spark/pull/22309#discussion_r228716352
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
---
@@ -635,13 +675,17 @@ object ScalaReflection extends ScalaReflection {
"cannot be used as field name\n" +
walkedTypePath.mkString("\n"))
}
+ // as a field, value class is represented by its underlying type
+ val trueFieldType =
+ if (isValueClass(fieldType)) getUnderlyingTypeOf(fieldType)
else fieldType
+
val fieldValue = Invoke(
- AssertNotNull(inputObject, walkedTypePath), fieldName,
dataTypeFor(fieldType),
- returnNullable = !fieldType.typeSymbol.asClass.isPrimitive)
- val clsName = getClassNameFromType(fieldType)
+ AssertNotNull(inputObject, walkedTypePath), fieldName,
dataTypeFor(trueFieldType),
+ returnNullable = !trueFieldType.typeSymbol.asClass.isPrimitive)
+ val clsName = getClassNameFromType(trueFieldType)
--- End diff --
I tried moving the special logic to the value class case but have a concern
I don't know how to resolve yet. I need to change `dataTypeFor` to return
`ObjectType` for top level value class and `dataTypeFor(underlyingType)`
otherwise (see my
[comment](https://github.com/apache/spark/pull/22309#discussion_r226142827)).
I'm going with something like this:
```scala
private def dataTypeFor(tpe: `Type`, isTopLevelValueClass: Boolean = true)
```
but this isn't right because:
- the default value `true` doesn't make sense for other types
- if default is `false` or there is no default value, many places that call
this method need to be changed
- it also feels clunky because `dataTypeFor` now has to be aware of the
context of its parameter
Do you have any suggestion on this?
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