Github user maropu commented on a diff in the pull request:
https://github.com/apache/spark/pull/17848#discussion_r115253834
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala ---
@@ -144,11 +181,24 @@ class UDFRegistration private[sql] (functionRegistry:
FunctionRegistry) extends
val inputTypes = Try(ScalaReflection.schemaFor[A1].dataType ::
Nil).toOption
def builder(e: Seq[Expression]) = ScalaUDF(func, dataType, e,
inputTypes.getOrElse(Nil), Some(name))
functionRegistry.registerFunction(name, builder)
- UserDefinedFunction(func, dataType, inputTypes)
+ UserDefinedFunction(func, dataType, inputTypes, Some(name))
}
/**
- * Register a Scala closure of 2 arguments as user-defined function
(UDF).
+ * Registers a Scala closure of 1 arguments as user-defined function
(UDF).
+ * @tparam RT return type of UDF.
+ * @since 2.3.0
+ */
+ def register[RT: TypeTag, A1: TypeTag](name: String, func: Function1[A1,
RT], deterministic : Boolean, distinctLike: Boolean): UserDefinedFunction = {
--- End diff --
I basically think these parameters are useful for users though, do we
always need to set `deterministic` and `distinctLike` when registering UDFs?
ISTM this is a little annoying for users, so we'd better to use default
parameters for these parameters?
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