nchammas commented on code in PR #45363:
URL: https://github.com/apache/spark/pull/45363#discussion_r1510555559
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connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala:
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@@ -259,18 +259,19 @@ class RelationalGroupedDataset private[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")
* }}}
*
+ * @note
Review Comment:
I trimmed the note a bit. Is that better?
I also took a look at the CSV reader method:
https://github.com/apache/spark/blob/a1b0da200b271214e9d6b3170308509d7d514c7f/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala#L530-L532
It's pretty similar to what I'm proposing here.
I believe it's more important to highlight the eager computation here since
`pivot` is a transformation and, unlike with reader methods, users are probably
not expecting expensive computations to be triggered. But I agree, we don't
want to make it sound like there's something _wrong_ with not specifying pivot
values.
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