zhengruifeng commented on code in PR #58124:
URL: https://github.com/apache/spark/pull/58124#discussion_r3821430135
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sql/api/src/main/scala/org/apache/spark/sql/functions.scala:
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@@ -17723,6 +17723,32 @@ object functions {
*/
def unwrap_udt(column: Column): Column = Column.internalFn("unwrap_udt",
column)
+ /**
+ * Wrap a column as a user-defined type.
+ * @param column
+ * the column to wrap. The column data type must match the UDT's
underlying SQL type.
+ * @param udt
+ * the target user-defined type.
+ * @group udf_funcs
+ * @since 4.4.0
+ */
+ def wrap_udt(column: Column, udt: UserDefinedType[_]): Column = {
+ wrap_udt(column, lit(udt.json))
+ }
+
+ /**
+ * Wrap a column as a user-defined type.
+ * @param column
+ * the column to wrap. The column data type must match the UDT's
underlying SQL type.
+ * @param udt
+ * the target user-defined type as a constant JSON string column.
+ * @group udf_funcs
+ * @since 4.4.0
+ */
+ def wrap_udt(column: Column, udt: Column): Column = {
Review Comment:
Thanks for the suggestion. I agree `udtJsonColumn` is more explicit, but it
seems conventional in this file to keep the semantic argument name stable
across overloads when only the accepted representation changes.
For example, `from_json` keeps the parameter name as `schema` across
`StructType`, `DataType`, `String`, and `Column` overloads, and `from_csv` does
the same for `StructType` and `Column` schema overloads. Following that
convention, I would prefer to keep this overload as `udt` and clarify in the
Scaladoc that the `Column` must be a constant UDT JSON string.
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