Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/7841#discussion_r44500612
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala ---
@@ -273,6 +280,60 @@ class GroupedData protected[sql](
def sum(colNames: String*): DataFrame = {
aggregateNumericColumns(colNames : _*)(Sum)
}
+
+ /**
+ * (Scala-specific) Pivots a column of the current [[DataFrame]] and
preform the specified
+ * aggregation.
+ * {{{
+ * // Compute the sum of earnings for each year by course with each
course as a separate column
+ * df.groupBy($"year").pivot($"course", "dotNET",
"Java").agg(sum($"earnings"))
+ * // Or without specifying column values
+ * df.groupBy($"year").pivot($"course").agg(sum($"earnings"))
+ * }}}
--- End diff --
Another question I have is that, what will happen if we have too many
distinct values? I am wondering if we should always ask users to put pivot
values?
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