Github user sun-rui commented on a diff in the pull request:
https://github.com/apache/spark/pull/12426#discussion_r60359396
--- Diff: R/pkg/R/context.R ---
@@ -225,3 +225,26 @@ broadcast <- function(sc, object) {
setCheckpointDir <- function(sc, dirName) {
invisible(callJMethod(sc, "setCheckpointDir",
suppressWarnings(normalizePath(dirName))))
}
+
+#' @title Run a function over a list of elements, distributing the
computations with Spark.
+#'
+#' @description
+#' Applies a function in a manner that is similar to doParallel or lapply
to elements of a list.
+#' The computations are distributed using Spark. It is conceptually the
same as the following code:
+#' unlist(lapply(list, func))
+#'
+#' @param list the list of elements
+#' @param func a function that takes one argument.
+#' @noRd
+#' @examples
+#' Here is a trivial example that double the values in a list
+#'\dontrun{
+#' doubled <- sparkLapply(1:10, function(x){2 * x})
+#'}
+sparkLapply <- function(list, func) {
+ sc <- get(".sparkRjsc", envir = .sparkREnv)
+ rdd <- parallelize(sc, list, length(list))
--- End diff --
dapply and spark.lapply have different schematics. No need to check
class(list) here as a DataFrame can be treated as a list of columns.
parallelize() will issue warning for DataFrame at here:
https://github.com/apache/spark/blob/master/R/pkg/R/context.R#L110
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