Github user HyukjinKwon commented on a diff in the pull request:

    https://github.com/apache/spark/pull/15072#discussion_r82738407
  
    --- 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 --
    
    Ah, the reason why `intersect` was fine seems because it does contains any 
data. `intersect` seems also failed:
    
    ```
    val dates = Seq(Tuple1(BigDecimal.valueOf(10.5))).toDF("decimal")
    val widenTypedRows =  Seq(Tuple1(10.5D)).toDF("decimal")
    dates.intersect(widenTypedRows).collect()
    ```


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