Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/16668#discussion_r97209653
--- 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 --
Right, we agreed.
The conversion, especially into RRDD, is in particular concerning. From
what I can see though this `df.rdd.getNumPartitions` is the recommended
practice, which seems to be all over pyspark. (granted, DataFrame to RDD in
pyspark is likely slightly more efficient)
An alternative, is we could wrap all of this on the JVM side - at least
that should save us the around trip to RRDD.
But agreed, is there a more efficient way this could be exposed in
DataFrame/Dataset directly instead?
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