JacobZheng0927 commented on code in PR #43813:
URL: https://github.com/apache/spark/pull/43813#discussion_r1398808757
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sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala:
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@@ -1960,6 +1987,42 @@ class Dataset[T] private[sql](
toDF(), colNames.map(colName => resolve(colName)),
RelationalGroupedDataset.CubeType)
}
+ /**
+ * Create multi-dimensional aggregation for the current Dataset using the
specified grouping sets,
+ * so we can run aggregation on them.
+ * See [[RelationalGroupedDataset]] for all the available aggregate
functions.
+ *
+ * This is a variant of groupingSets that can only group by existing columns
using column names
+ * (i.e. cannot construct expressions).
+ *
+ * {{{
+ * // Compute the average for all numeric columns group by specific
grouping sets.
+ * ds.groupingSets(Seq(Seq("department", "group"),Seq()),"department",
"group").avg()
+ *
+ * // Compute the max age and average salary, group by specific grouping
sets.
+ * ds.groupingSets(Seq($"department", $"gender"), Seq($"department"),
Seq()).agg(Map(
+ * "salary" -> "avg",
+ * "age" -> "max"
+ * ))
+ * }}}
+ *
+ * @group untypedrel
+ * @since 4.0.0
+ */
+ @scala.annotation.varargs
+ def groupingSets(
+ groupingSets: Seq[Seq[String]],
Review Comment:
Are you saying that the methods using string columns are no longer needed,
and that I only need to keep the methods using columns of type Column?
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