Github user maropu commented on a diff in the pull request:
https://github.com/apache/spark/pull/18300#discussion_r122645208
--- 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 --
In that case, I think we couldn't pass `unionPlan.assertAnalyzed()`?
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