Github user dongjoon-hyun commented on a diff in the pull request:
https://github.com/apache/spark/pull/13545#discussion_r66152341
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
@@ -2262,6 +2275,19 @@ class Dataset[T] private[sql](
def distinct(): Dataset[T] = dropDuplicates()
/**
+ * Returns a new [[Dataset]] that contains only the unique rows from
this [[Dataset]], considering
+ * only the subset of columns. This is an alias for
`dropDuplicates(cols)`.
+ *
+ * Note that, equality checking is performed directly on the encoded
representation of the data
+ * and thus is not affected by a custom `equals` function defined on `T`.
+ *
+ * @group typedrel
+ * @since 2.0.0
+ */
+ @scala.annotation.varargs
+ def distinct(cols: String*): Dataset[T] = dropDuplicates(cols)
--- End diff --
Thank you always for fast feedbacks, @rxin . And for nice lunch. :)
Yes, right. For this, maybe it's not needed because `distinct` is usually
used with `select`.
Also, we can use `dropDuplicates` since it's just an alias of
`dropDuplicates`.
I think `distinct` is a function name which is more consistent with SQL. If
we have this, we can do this, too.
```
ds.select("_1", "_2", "_3").distinct("_1").orderBy("_1", "_2").show()
```
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