Github user shivaram commented on a diff in the pull request:
https://github.com/apache/spark/pull/8276#discussion_r37465795
--- Diff: R/pkg/R/DataFrame.R ---
@@ -628,18 +628,49 @@ setMethod("dim",
setMethod("collect",
signature(x = "DataFrame"),
function(x, stringsAsFactors = FALSE) {
- # listCols is a list of raw vectors, one per column
- listCols <- callJStatic("org.apache.spark.sql.api.r.SQLUtils",
"dfToCols", x@sdf)
- cols <- lapply(listCols, function(col) {
- objRaw <- rawConnection(col)
- numRows <- readInt(objRaw)
- col <- readCol(objRaw, numRows)
- close(objRaw)
- col
- })
- names(cols) <- columns(x)
- do.call(cbind.data.frame, list(cols, stringsAsFactors =
stringsAsFactors))
- })
+ names <- columns(x)
+ ncol <- length(names)
+ if (ncol <= 0) {
+ # empty data.frame with 0 columns and 0 rows
+ data.frame()
+ } else {
+ # listCols is a list of columns
+ listCols <-
callJStatic("org.apache.spark.sql.api.r.SQLUtils", "dfToCols", x@sdf)
+ stopifnot(length(listCols) == ncol)
+
+ # An empty data.frame with 0 columns and number of rows as
collected
+ nrow <- length(listCols[[1]])
+ if (nrow <= 0) {
+ df <- data.frame()
+ } else {
+ df <- data.frame(row.names = c(1 : nrow))
+ }
+
+ # Append columns one by one
+ for (colIndex in 1 : ncol) {
+ # Note: appending a column of list type into a data.frame
so that
+ # data of complex type can be held. But getting a cell
from a column
+ # of list type returns a list instead of a vector. So for
columns of
+ # non-complex type, append them as vector.
+ col <- listCols[[colIndex]]
+ if (length(col) <= 0) {
+ df[[names[colIndex]]] <- col
--- End diff --
this is just to handle empty columns ? Can't we just return from line 644
if one of the columns is empty ? Or do we want to handle cases where some
columns are empty and some are not etc.
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