Github user koertkuipers commented on the issue:
https://github.com/apache/spark/pull/13512
@cloud-fan from the (added) unit tests:
```
val df2 = Seq("a" -> 1, "a" -> 3, "b" -> 3).toDF("i", "j")
checkAnswer(df2.groupBy("i").agg(ComplexResultAgg.toColumn),
Row("a", Row(2, 4)) :: Row("b", Row(1, 3)) :: Nil)
```
this shows how the underlying type is Row (with a schema consisting of
Strings and Ints), and it gets converted to the input type of the Aggregator
which is (String, Long), so this involves both conversion and upcast.
and:
```
val df3 = Seq(("a", "x", 1), ("a", "y", 3), ("b", "x", 3)).toDF("i", "j",
"k")
checkAnswer(df3.groupBy("i").agg(ComplexResultAgg("i", "k")),
Row("a", Row(2, 4)) :: Row("b", Row(1, 3)) :: Nil)
```
this is similar to the previous example but i also select the columns i
want the Aggregator to operate on (namely columns "i" and "k")
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