Github user maropu commented on a diff in the pull request:
https://github.com/apache/spark/pull/21699#discussion_r199742580
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala ---
@@ -340,36 +340,52 @@ class RelationalGroupedDataset protected[sql](
/**
* Pivots a column of the current `DataFrame` and performs the specified
aggregation.
- * There are two versions of pivot function: one that requires the
caller to specify the list
- * of distinct values to pivot on, and one that does not. The latter is
more concise but less
- * efficient, because Spark needs to first compute the list of distinct
values internally.
*
* {{{
* // Compute the sum of earnings for each year by course with each
course as a separate column
- * df.groupBy("year").pivot("course", Seq("dotNET",
"Java")).sum("earnings")
- *
- * // Or without specifying column values (less efficient)
- * df.groupBy("year").pivot("course").sum("earnings")
+ * df.groupBy($"year").pivot($"course", Seq("dotNET",
"Java")).sum($"earnings")
* }}}
*
- * @param pivotColumn Name of the column to pivot.
+ * @param pivotColumn the column to pivot.
* @param values List of values that will be translated to columns in
the output DataFrame.
- * @since 1.6.0
+ * @since 2.4.0
*/
- def pivot(pivotColumn: String, values: Seq[Any]):
RelationalGroupedDataset = {
+ def pivot(pivotColumn: Column, values: Seq[Any]):
RelationalGroupedDataset = {
--- End diff --
To make diffs smaller, can you move this under the signature `def
pivot(pivotColumn: String, values: Seq[Any])`?
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
