Optional aggregation columns shortcut the computation to an empty dataset

so it seems that if during an aggregation the first aggregate returns null, then it doesn't even care about the next results, it just returns nothing. Going to look if this is a Spark bug and not a Frameless one. Nope, the issue seems to be on our side of the fence

case class X[A,B](a: A, b: B)
val t = TypedDataset.create(X[Option[Int],Long](None, 0)::Nil)
t.show().run()
+----+---+
|   a|  b|
+----+---+
|null|  0|
+----+---+

scala> t.agg(first(t('a)), sum(t('b))).collect().run()
res: Seq[(Option[Int], Long)] = WrappedArray()

scala> t.agg(sum(t('b)), first(t('a))).collect().run()
res: Seq[(Long, Option[Int])] = WrappedArray((0,None))

Test that fails randomly due to this issue (NonAggregateFunctionsTests.scala).

 test("Empty vararg tests") {
    import frameless.functions.aggregate._
    def prop[A : TypedEncoder, B: TypedEncoder](data: Vector[X2[A, B]]) = {
      val ds = TypedDataset.create(data)
      val frameless = ds.select(ds('a), concat(), ds('b), concatWs(":")).collect().run().toVector
      val framelessAggr = ds.agg(first(ds('a)), concat(), concatWs("x"), litAggr(2)).collect().run().toVector
      val scala = data.map(x => (x.a, "", x.b, ""))
      val scalaAggr = if (data.nonEmpty) Vector((data.head.a, "", "", 2)) else Vector.empty
      (frameless ?= scala).&&(framelessAggr ?= scalaAggr)
    }

    check(forAll(prop[Long, Long] _))
    check(forAll(prop[Option[Vector[Boolean]], Long] _))
  }

Start with NonAggregateFunctionsTests.scala and run the "Empty vararg tests" property test to reproduce the aggregation result. Trace the aggregation path used by TypedDataset.agg and collect, focusing on cases where first(ds('a')) returns null. Done means aggregate columns are all evaluated and the expected tuple is returned regardless of aggregate order.