Github user dongjoon-hyun commented on a diff in the pull request:
https://github.com/apache/spark/pull/13774#discussion_r67672158
--- Diff: R/pkg/R/functions.R ---
@@ -911,6 +911,33 @@ setMethod("minute",
column(jc)
})
+#' monotonically_increasing_id
+#'
+#' Return a column that generates monotonically increasing 64-bit integers.
+#'
+#' The generated ID is guaranteed to be monotonically increasing and
unique, but not consecutive.
+#' The current implementation puts the partition ID in the upper 31 bits,
and the record number
+#' within each partition in the lower 33 bits. The assumption is that the
data frame has
+#' less than 1 billion partitions, and each partition has less than 8
billion records.
+#'
+#' As an example, consider a SparkDataFrame with two partitions, each with
3 records.
+#' This expression would return the following IDs:
+#' 0, 1, 2, 8589934592 (1L << 33), 8589934593, 8589934594.
+#'
+#' This is equivalent to the MONOTONICALLY_INCREASING_ID function in SQL.
+#'
+#' @rdname monotonically_increasing_id
+#' @name monotonically_increasing_id
+#' @family misc_funcs
+#' @export
+#' @examples \dontrun{select(df, monotonically_increasing_id())}
+setMethod("monotonically_increasing_id",
+ signature(x = "missing"),
+ function(x) {
--- End diff --
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