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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