dilipbiswal commented on a change in pull request #24151:
[SPARK-26739][SQL][WIP] Standardized Join Types for DataFrames
URL: https://github.com/apache/spark/pull/24151#discussion_r267194219
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
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@@ -927,29 +927,28 @@ class Dataset[T] private[sql](
*
* @param right Right side of the join operation.
* @param usingColumns Names of the columns to join on. This columns must
exist on both sides.
- * @param joinType Type of join to perform. Default `inner`. Must be one of:
- * `inner`, `cross`, `outer`, `full`, `full_outer`, `left`,
`left_outer`,
- * `right`, `right_outer`, `left_semi`, `left_anti`.
- *
+ * @param joinType Type of join to perform. Default `Inner`. Must be one of
the valid JoinType:
+ * `Inner`, `Cross`, `FullOuter`, `LeftOuter`, `RightOuter`,
+ * `LeftSemi`, `LeftAnti`.
* @note If you perform a self-join using this function without aliasing the
input
* `DataFrame`s, you will NOT be able to reference any columns after the
join, since
* there is no way to disambiguate which side of the join you would like to
reference.
*
* @group untypedrel
* @since 2.0.0
*/
- def join(right: Dataset[_], usingColumns: Seq[String], joinType: String):
DataFrame = {
+ def join(right: Dataset[_], usingColumns: Seq[String], joinType: JoinType):
DataFrame = {
Review comment:
Isn't this a breaking change ? Are we allowed to do that ? cc @HyukjinKwon
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