Github user ueshin commented on a diff in the pull request:
https://github.com/apache/spark/pull/22646#discussion_r225807075
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala ---
@@ -1115,9 +1126,38 @@ object SQLContext {
})
}
}
- def createConverter(cls: Class[_], dataType: DataType): Any => Any =
dataType match {
- case struct: StructType => createStructConverter(cls,
struct.map(_.dataType))
- case _ => CatalystTypeConverters.createToCatalystConverter(dataType)
+ def createConverter(t: Type, dataType: DataType): Any => Any = (t,
dataType) match {
+ case (cls: Class[_], struct: StructType) =>
--- End diff --
Reusing `JavaTypeInference.serializerFor` would be great, but currently it
behaves a little differently. At least it doesn't support
`java.lang.Iterable[_]`, so we can't use it immediately. We need to extend it
to support `Iterable` (and also `deserializerFor`).
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