Github user NarineK commented on a diff in the pull request:
https://github.com/apache/spark/pull/12887#discussion_r62148622
--- Diff: R/pkg/R/DataFrame.R ---
@@ -570,10 +570,17 @@ setMethod("unpersist",
#' Repartition
#'
-#' Return a new SparkDataFrame that has exactly numPartitions partitions.
-#'
+#' The following options for repartition are possible:
+#' \itemize{
+#' \item{"Option 1"} {Return a new SparkDataFrame partitioned by
+#' the given columns into `numPartitions`.}
+#' \item{"Option 2"} {Return a new SparkDataFrame that has exactly
`numPartitions`.}
+#' \item{"Option 3"} {Return a new SparkDataFrame partitioned by the
given columns,
+#' preserving the existing number of partitions.}
--- End diff --
Yes, @shivaram , I did refer to scala doc in Dataset.scala.
I the reality for the case - repartition(col1, col2) - internally in the
logical plan `spark.sql.shuffle.partitions` is being used
```
/**
* This method repartitions data using [[Expression]]s into
`numPartitions`, and receives
* information about the number of partitions during execution. Used when a
specific ordering or
* distribution is expected by the consumer of the query result. Use
[[Repartition]] for RDD-like
* `coalesce` and `repartition`.
* If `numPartitions` is not specified, the number of partitions will be
the number set by
* `spark.sql.shuffle.partitions`.
*/
case class RepartitionByExpression(
partitionExpressions: Seq[Expression],
child: LogicalPlan,
numPartitions: Option[Int] = None) extends RedistributeData {
numPartitions match {
case Some(n) => require(n > 0, "numPartitions must be greater than 0.")
case None => // Ok
}
}
```
https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala#L38
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