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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