Github user shivaram commented on a diff in the pull request:
https://github.com/apache/spark/pull/16668#discussion_r97209406
--- Diff: R/pkg/R/DataFrame.R ---
@@ -3406,3 +3406,28 @@ setMethod("randomSplit",
}
sapply(sdfs, dataFrame)
})
+
+#' getNumPartitions
+#'
+#' Return the number of partitions
+#' Note: in order to compute the number of partition the SparkDataFrame
has to be converted into a
+#' RDD temporarily internally.
+#'
+#' @param x A SparkDataFrame
+#' @family SparkDataFrame functions
+#' @aliases getNumPartitions,SparkDataFrame-method
+#' @rdname getNumPartitions
+#' @name getNumPartitions
+#' @export
+#' @examples
+#'\dontrun{
+#' sparkR.session()
+#' df <- createDataFrame(cars, numPartitions = 2)
+#' getNumPartitions(df)
+#' }
+#' @note getNumPartitions since 2.1.1
+setMethod("getNumPartitions",
+ signature(x = "SparkDataFrame"),
+ function(x) {
+ getNumPartitionsRDD(toRDD(x))
--- End diff --
As discussed in the JIRA I worry that this will be a very expensive
operation for large data frames. Specifically instead of create an RRDD, can we
do some operations on the Scala side which might be cheaper ?
cc @yhuai @cloud-fan who know more about DataFrame on the SQL side
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