felixcheung commented on a change in pull request #23787: [SPARK-26830][SQL][R]
Vectorized R dapply() implementation
URL: https://github.com/apache/spark/pull/23787#discussion_r259236795
##########
File path: R/pkg/R/DataFrame.R
##########
@@ -1437,6 +1437,29 @@ dapplyInternal <- function(x, func, schema) {
schema <- structType(schema)
}
+ arrowEnabled <- sparkR.conf("spark.sql.execution.arrow.enabled")[[1]] ==
"true"
+ if (arrowEnabled) {
+ requireNamespace1 <- requireNamespace
+ if (!requireNamespace1("arrow", quietly = TRUE)) {
+ stop("'arrow' package should be installed.")
+ }
+ # Currenty Arrow optimization does not support raw for now.
+ # Also, it does not support explicit float type set by users.
+ if (inherits(schema, "structType")) {
+ if (any(sapply(schema$fields(), function(x) x$dataType.toString() ==
"FloatType"))) {
+ stop("Arrow optimization with dapply do not support FloatType yet.")
+ }
+ if (any(sapply(schema$fields(), function(x) x$dataType.toString() ==
"BinaryType"))) {
+ stop("Arrow optimization with dapply do not support BinaryType yet.")
+ }
+ } else if (is.null(schema)) {
+ stop(paste0("Arrow optimization does not support 'dapplyCollect' yet.
Please use ",
+ "'collect' and 'dapply' APIs instead."))
Review comment:
this isn't quite the same though, for example, dapply will need the user to
pass in a schema. should we suggest turning off arrow then use dapplyCollect?
I'm actually not sure - maybe suggest both in the message?
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