Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/18300#discussion_r123411528
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
@@ -1764,6 +1765,70 @@ 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 {
+ // Resolves children first to reorder output attributes in `other` by
name
+ val leftPlan = sparkSession.sessionState.executePlan(logicalPlan)
+ val rightPlan =
sparkSession.sessionState.executePlan(other.logicalPlan)
--- End diff --
I think a Dataset already guarantees its plan is analyzed and passes check?
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
