hvanhovell commented on code in PR #40057:
URL: https://github.com/apache/spark/pull/40057#discussion_r1109131122
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connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/Dataset.scala:
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@@ -1035,6 +1035,29 @@ class Dataset[T] private[sql] (val session:
SparkSession, private[sql] val plan:
}
}
+ /**
+ * Groups the Dataset using the specified columns, so we can run aggregation
on them. See
+ * [[RelationalGroupedDataset]] for all the available aggregate functions.
+ *
+ * {{{
+ * // Compute the average for all numeric columns grouped by department.
+ * ds.groupBy($"department").avg()
+ *
+ * // Compute the max age and average salary, grouped by department and
gender.
+ * ds.groupBy($"department", $"gender").agg(Map(
+ * "salary" -> "avg",
+ * "age" -> "max"
+ * ))
+ * }}}
+ *
+ * @group untypedrel
+ * @since 3.4.0
+ */
+ @scala.annotation.varargs
+ def groupBy(cols: Column*): RelationalGroupedDataset = {
+ RelationalGroupedDataset(toDF(), cols.map(_.expr))
Review Comment:
Nit: you don't have to convert to DataFrame here (as soon as we introduce
encoders it might actually be a bit faster if we don't). You could also pass in
the columns as is.
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