Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/15559#discussion_r84223480
--- Diff: R/pkg/R/DataFrame.R ---
@@ -2285,23 +2285,23 @@ setMethod("dropDuplicates",
#' @aliases join,SparkDataFrame,SparkDataFrame-method
#' @rdname join
#' @name join
-#' @seealso \link{merge}
+#' @seealso \link{merge} \link{crossJoin}
#' @export
#' @examples
#'\dontrun{
#' sparkR.session()
#' df1 <- read.json(path)
#' df2 <- read.json(path2)
-#' join(df1, df2) # Performs a Cartesian
#' join(df1, df2, df1$col1 == df2$col2) # Performs an inner join based on
expression
#' join(df1, df2, df1$col1 == df2$col2, "right_outer")
+#' crossJoin(df1, df2) # Performs a Cartesian
#' }
#' @note join since 1.4.0
setMethod("join",
signature(x = "SparkDataFrame", y = "SparkDataFrame"),
function(x, y, joinExpr = NULL, joinType = NULL) {
if (is.null(joinExpr)) {
- sdf <- callJMethod(x@sdf, "crossJoin", y@sdf)
+ stop("use crossJoin for Cartesian join")
--- End diff --
this is actually not the behavior in Python/Scala.
The behavior should be that users can still call join with any condition,
but when it actually comes to an action, the planner checks whether this can be
converted into a non-cartesian join (via predicates on top of the join).
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