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

    https://github.com/apache/spark/pull/18300#discussion_r122645584
  
    --- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
    @@ -1764,6 +1765,58 @@ class Dataset[T] private[sql](
       }
     
       /**
    +   * Returns a new Dataset containing union of rows in this Dataset and 
another Dataset.
    +   *
    +   * This is different from both `UNION ALL` and `UNION DISTINCT` in SQL. 
To do a SQL-style set
    +   * union (that does deduplication of elements), use this function 
followed by a [[distinct]].
    +   *
    +   * The difference between this function and [[union]] is that this 
function
    +   * resolves columns by name (not by position):
    +   *
    +   * {{{
    +   *   val df1 = Seq((1, 2, 3)).toDF("col0", "col1", "col2")
    +   *   val df2 = Seq((4, 5, 6)).toDF("col1", "col2", "col0")
    +   *   df1.unionByName(df2).show
    +   *
    +   *   // output:
    +   *   // +----+----+----+
    +   *   // |col0|col1|col2|
    +   *   // +----+----+----+
    +   *   // |   1|   2|   3|
    +   *   // |   6|   4|   5|
    +   *   // +----+----+----+
    +   * }}}
    +   *
    +   * @group typedrel
    +   * @since 2.3.0
    +   */
    +  def unionByName(other: Dataset[T]): Dataset[T] = withSetOperator {
    +    // Creates a `Union` node and resolves it first to reorder output 
attributes in `other` by name
    +    val unionPlan = 
sparkSession.sessionState.executePlan(Union(logicalPlan, other.logicalPlan))
    --- End diff --
    
    Yeah, so we don't plan to support it?


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