HyukjinKwon commented on a change in pull request #23746: [SPARK-26761][SQL][R]
Vectorized R gapply() implementation
URL: https://github.com/apache/spark/pull/23746#discussion_r255298489
##########
File path: R/pkg/inst/worker/worker.R
##########
@@ -181,17 +185,35 @@ if (isEmpty != 0) {
colNames, computeFunc, data)
} else {
# gapply mode
+ outputs <- list()
for (i in 1:length(data)) {
# Timing reading input data for execution
inputElap <- elapsedSecs()
output <- compute(mode, partition, serializer, deserializer,
keys[[i]],
colNames, computeFunc, data[[i]])
computeElap <- elapsedSecs()
- outputResult(serializer, output, outputCon)
+ if (deserializer == "arrow") {
+ requireNamespace("arrow", quietly = TRUE)
+ outputs[[length(outputs) + 1L]] <- output
+ } else {
+ outputResult(serializer, output, outputCon)
+ }
outputElap <- elapsedSecs()
computeInputElapsDiff <- computeInputElapsDiff + (computeElap -
inputElap)
outputComputeElapsDiff <- outputComputeElapsDiff + (outputElap -
computeElap)
}
+
+ if (deserializer == "arrow") {
+ requireNamespace("arrow", quietly = TRUE)
+ # See
https://stat.ethz.ch/pipermail/r-help/2010-September/252046.html
+ # rbind.fill might be an anternative to make it faster if plyr is
installed.
+ combined <- do.call("rbind", outputs)
Review comment:
Actually, according to the link above, the fastest way is convert them as
matrix. I tried it before but looks it's not able to keep its types apparently.
I tried other ways too but this seems to be the only way.
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