Github user shivaram commented on a diff in the pull request:
https://github.com/apache/spark/pull/9012#discussion_r42674571
--- Diff: R/pkg/R/DataFrame.R ---
@@ -1457,15 +1457,142 @@ setMethod("join",
dataFrame(sdf)
})
-#' @rdname merge
+#'
#' @name merge
#' @aliases join
+#' @title Merges two data frames
+#' @param x the first data frame to be joined
+#' @param y the second data frame to be joined
+#' @param by a character vector specifying the join columns. If by is not
+#' specified, the common column names in \code{x} and \code{y} will be
used.
+#' @param by.x a character vector specifying the joining columns for x.
+#' @param by.y a character vector specifying the joining columns for y.
+#' @param all.x a boolean value indicating whether all the rows in x should
+#' be including in the join
+#' @param all.y a boolean value indicating whether all the rows in y should
+#' be including in the join
+#' @param sort a logical argument indicating whether the resulting columns
should be sorted
+#' @details If all.x and all.y are set to FALSE, a natural join will be
returned. If
+#' all.x is set to TRUE and all.y is set to FALSE, a left outer join will
+#' be returned. If all.x is set to FALSE and all.y is set to TRUE, a
right
+#' outer join will be returned. If all.x and all.y are set to TRUE, a
full
+#' outer join will be returned.
+#' @rdname merge
+#' @export
+#' @examples
+#'\dontrun{
+#' sc <- sparkR.init()
+#' sqlContext <- sparkRSQL.init(sc)
+#' df1 <- jsonFile(sqlContext, path)
+#' df2 <- jsonFile(sqlContext, path2)
+#' merge(df1, df2) # Performs a Cartesian
+#' merge(df1, df2, by = "col1") # Performs an inner join based on
expression
+#' merge(df1, df2, by.x = "col1", by.y = "col2", all.y = TRUE)
+#' merge(df1, df2, by.x = "col1", by.y = "col2", all.x = TRUE)
+#' merge(df1, df2, by.x = "col1", by.y = "col2", all.x = TRUE, all.y =
TRUE)
+#' merge(df1, df2, by.x = "col1", by.y = "col2", all = TRUE, sort = FALSE)
+#' merge(df1, df2, by = "col1", all = TRUE, suffixes = c("-X", "-Y"))
+#' }
setMethod("merge",
signature(x = "DataFrame", y = "DataFrame"),
- function(x, y, joinExpr = NULL, joinType = NULL, ...) {
- join(x, y, joinExpr, joinType)
- })
+ function(x, y, by = intersect(names(x), names(y)), by.x = by,
by.y = by,
+ all = FALSE, all.x = all, all.y = all,
+ sort = TRUE, suffixes = c("_x","_y"), ... ) {
+
+ if (length(suffixes) != 2) {
+ stop("suffixes must have length 2")
+ }
+
+ # join type is identified based on the values of all, all.x
and all.y
+ # default join type is inner, according to R it should be
natural but since it
+ # is not supported in spark inner join is used
+ joinType <- "inner"
+ if (all || (all.x && all.y)) {
+ joinType <- "outer"
+ } else if (all.x) {
+ joinType <- "left_outer"
+ } else if (all.y) {
+ joinType <- "right_outer"
+ }
+ # join expression is based on by.x, by.y if both by.x and by.y
are not missing
+ # or on by, if by.x or by.y are missing or have different
lengths
+ if (length(by.x) > 0 && length(by.x) == length(by.y)) {
+ joinX <- by.x
+ joinY <- by.y
+ } else if (length(by) > 0) {
+ joinX <- by
+ joinY <- by
+ } else {
+ # if by or both by.x and by.y have length 0, use Cartesian
Product
+ joinRes <- join(x, y)
+ return (joinRes)
+ }
+
+ # sets alias for making colnames unique in dataframes 'x' and
'y'
+ colsX <- generateAliasesForIntersectedCols(x, by, suffixes[1])
+ colsY <- generateAliasesForIntersectedCols(y, by, suffixes[2])
+
+ # selects columns with their aliases from dataframes
+ # in case same column names are present in both data frames
+ xsel <- select(x, colsX)
+ ysel <- select(y, colsY)
+
--- End diff --
Minor comment: Could you write a comment here on what this code block is
doing ?
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