Github user HyukjinKwon commented on a diff in the pull request: https://github.com/apache/spark/pull/15072#discussion_r82728292 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala --- @@ -53,7 +53,15 @@ import org.apache.spark.util.Utils private[sql] object Dataset { def apply[T: Encoder](sparkSession: SparkSession, logicalPlan: LogicalPlan): Dataset[T] = { - new Dataset(sparkSession, logicalPlan, implicitly[Encoder[T]]) + val encoder = implicitly[Encoder[T]] + if (encoder.clsTag.runtimeClass == classOf[Row]) { + // We should use the encoder generated from the executed plan rather than the existing + // encoder for DataFrame because the types of columns can be varied due to widening types. + // See SPARK-17123. This is a bit hacky. Maybe we should find a better way to do this. + ofRows(sparkSession, logicalPlan).asInstanceOf[Dataset[T]] + } else { + new Dataset(sparkSession, logicalPlan, encoder) + } --- End diff -- Hm, I manually tested. It seems `except` is failed too. It seems fine with `intersect`.
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