Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/9565#discussion_r44509246
--- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala ---
@@ -306,7 +306,15 @@ trait Row extends Serializable {
*
* @throws ClassCastException when data type does not match.
*/
- def getStruct(i: Int): Row = getAs[Row](i)
+ def getStruct(i: Int): Row = {
+ // Product and Row both are recoginized as StructType in a Row
+ val t = get(i)
+ if (t.isInstanceOf[Product]) {
+ Row.fromTuple(t.asInstanceOf[Product])
+ } else {
+ t.asInstanceOf[Row]
+ }
+ }
--- End diff --
We use `schemaFor` to get a catalyst DataType for udf's return type. For
`Product` type, we return a `StructType` now. That causes problem in
`RowEncoder` because `RowEncoder` will try to get a `Row` not a `Product` for a
field of `StructType`. You will get a casting exception if your udf returns
something like `(1, 2)`.
The problem is a field of `StructType` in a `Row` can be a `Product` or a
`Row`. I modified the `getStruct` method in `Row` to turn a `Row` for a
`Product`.
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