best way to write Spark Dataset[U] - apache-spark

Question

Ok, so I am aware of the fact that Dataset.as[U] just changes the view of the Dataframe for typed operations.
As seen in this example:
case class One(one: Int)
val df = Seq(
(1,2,3),
(11,22,33),
(111,222,333)
).toDF("one", "two", "thre")
val ds : Dataset[One] = df.as[One]
ds.show
prints
+----+----+-----+
| one| two|three|
+----+----+-----+
| 1| 2| 3|
| 11| 22| 33|
| 111| 222| 333|
+----+----+-----+
This is totally fine and works in my favor most of the times. BUT now I need to write that ds to disk, with only that column one.
To enforce the schema I could do
.map(x => x) as this is a typed operation, the case class schema will take effect. This operation also results in a Dataset[One], but with the underlying data reduced to column one. This just seems awfully expensive looking at the execution plan
== Physical Plan ==
*SerializeFromObject [assertnotnull(input[0, $line2012488405320.$read$$iw$$iw$One, true]).one AS one#9408]
+- *MapElements <function1>, obj#9407: $line2012488405320.$read$$iw$$iw$One
+- *DeserializeToObject newInstance(class $line2012488405320.$read$$iw$$iw$One), obj#9406: $line2012488405320.$read$$iw$$iw$One
+- LocalTableScan [one#9391]
What are alternative implementations to achieve
ds.show
+----+
| one|
+----+
| 1|
| 11|
| 111|
+----+
UPDATE 1
I was thinking of a general solution to the problem. Maybe something along these lines?:
def caseClassAccessorNames[T <: Product](implicit tag: TypeTag[T]) = {
typeOf[T]
.members
.collect {
case m: MethodSymbol if m.isCaseAccessor => m.name
}
.map(m => m.toString)
}
def project[T <: Product](ds: Dataset[T])(implicit tag: TypeTag[T]): Dataset[T] = {
import ds.sparkSession.implicits._
val columnsOfT: Seq[Column] =
caseClassAccessorNames
.map(col)(scala.collection.breakOut)
val t: DataFrame = ds.select(columnsOfT: _*)
t.as[T]
}
I managed to get this working for the trivial example, but need to evaluate it further. I wonder if there are alternative, maybe built-in, ways to achieve something like this?