Github user wangyum commented on a diff in the pull request:
https://github.com/apache/spark/pull/22889#discussion_r230557937
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
@@ -883,6 +883,31 @@ class Dataset[T] private[sql](
join(right, Seq(usingColumn))
}
+ /**
+ * Equi-join with another `DataFrame` using the given column.
+ *
+ * Different from other join functions, the join column will only
appear once in the output,
+ * i.e. similar to SQL's `JOIN USING` syntax.
+ *
+ * {{{
+ * // Left join of df1 and df2 using the column "user_id"
+ * df1.join(df2, "user_id", "left")
+ * }}}
+ *
+ * @param right Right side of the join operation.
+ * @param usingColumn Name of the column to join on. This column 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`.
+ * @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
+ */
+ def join(right: Dataset[_], usingColumn: String, joinType: String):
DataFrame = {
--- End diff --
@arman1371 What do you think? ```def join(right: Dataset[_], usingColumn:
String, joinType: String)``` only support one column. right?
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