Github user jaceklaskowski commented on the issue:
https://github.com/apache/spark/pull/18523
Thanks @facaiy for the changes. I wonder if the code could `collect` all
the columns with incorrect type in one go (rather than reporting issues column
by column until a user fixed all).
Something along the lines:
```
val incorrectColumns = inputColNames
.map { name => (name, schema(name).dataType) }
.collect {
case (_, _: NumericType | BooleanType) =>
case t if t.isInstanceOf[VectorUDT] =>
case other => other }
// report issue with incorrectColumns
```
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