jonkeane commented on a change in pull request #10992:
URL: https://github.com/apache/arrow/pull/10992#discussion_r698728331
##########
File path: r/R/query-engine.R
##########
@@ -42,11 +55,73 @@ ExecPlan <- R6Class("ExecPlan",
}
# ScanNode needs the filter to do predicate pushdown and skip partitions,
# and it needs to know which fields to materialize (and which are
unnecessary)
- ExecNode_Scan(self, dataset, filter, colnames)
+ ExecNode_Scan(self, dataset, filter, colnames %||% character(0))
+ },
+ Build = function(.data) {
+ # This method takes an arrow_dplyr_query and chains together the
+ # ExecNodes that they produce. It does not evaluate them--that is Run().
+ group_vars <- dplyr::group_vars(.data)
+ grouped <- length(group_vars) > 0
+
+ # Collect the target names first because we have to add back the group
vars
+ target_names <- names(.data)
+ .data <- ensure_group_vars(.data)
+ .data <- ensure_arrange_vars(.data) # this sets .data$temp_columns
+
+ node <- self$Scan(.data)
+ # ARROW-13498: Even though Scan takes the filter, apparently we have to
do it again
+ if (inherits(.data$filtered_rows, "Expression")) {
+ node <- node$Filter(.data$filtered_rows)
+ }
+ # If any columns are derived we need to Project (otherwise this may be
no-op)
+ node <- node$Project(c(.data$selected_columns, .data$temp_columns))
+
+ if (length(.data$aggregations)) {
+ if (grouped) {
+ # We need to prefix all of the aggregation function names with
"hash_"
+ .data$aggregations <- lapply(.data$aggregations, function(x) {
+ x[["fun"]] <- paste0("hash_", x[["fun"]])
+ x
+ })
+ }
+
+ node <- node$Aggregate(
+ options = .data$aggregations,
+ target_names = target_names,
+ out_field_names = names(.data$aggregations),
+ key_names = group_vars
+ )
+
+ if (grouped) {
+ # The result will have result columns first then the grouping cols.
+ # dplyr orders group cols first, so adapt the result to meet that
expectation.
Review comment:
I agree with Ian that an option (with the default being no sort) would
be good here.
There's also some (very recent) discussion about removing this in dplyr (if
there's a new edition)
https://github.com/tidyverse/dplyr/issues/5664#issuecomment-907232443
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