1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
|
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# The following S3 methods are registered on load if dplyr is present
filter.arrow_dplyr_query <- function(.data, ..., .preserve = FALSE) {
# TODO something with the .preserve argument
filts <- quos(...)
if (length(filts) == 0) {
# Nothing to do
return(.data)
}
.data <- as_adq(.data)
# tidy-eval the filter expressions inside an Arrow data_mask
filters <- lapply(filts, arrow_eval, arrow_mask(.data))
bad_filters <- map_lgl(filters, ~ inherits(., "try-error"))
if (any(bad_filters)) {
# This is similar to abandon_ship() except that the filter eval is
# vectorized, and we apply filters that _did_ work before abandoning ship
# with the rest
expr_labs <- map_chr(filts[bad_filters], format_expr)
if (query_on_dataset(.data)) {
# Abort. We don't want to auto-collect if this is a Dataset because that
# could blow up, too big.
stop(
"Filter expression not supported for Arrow Datasets: ",
oxford_paste(expr_labs, quote = FALSE),
"\nCall collect() first to pull data into R.",
call. = FALSE
)
} else {
arrow_errors <- map2_chr(
filters[bad_filters], expr_labs,
handle_arrow_not_supported
)
if (length(arrow_errors) == 1) {
msg <- paste0(arrow_errors, "; ")
} else {
msg <- paste0("* ", arrow_errors, "\n", collapse = "")
}
warning(
msg, "pulling data into R",
immediate. = TRUE,
call. = FALSE
)
# Set any valid filters first, then collect and then apply the invalid ones in R
.data <- set_filters(.data, filters[!bad_filters])
return(dplyr::filter(dplyr::collect(.data), !!!filts[bad_filters]))
}
}
set_filters(.data, filters)
}
filter.Dataset <- filter.ArrowTabular <- filter.arrow_dplyr_query
set_filters <- function(.data, expressions) {
if (length(expressions)) {
if (is_list_of(expressions, "Expression")) {
# expressions is a list of Expressions. AND them together and set them on .data
new_filter <- Reduce("&", expressions)
} else if (inherits(expressions, "Expression")) {
new_filter <- expressions
} else {
stop("filter expressions must be either an expression or a list of expressions", call. = FALSE)
}
if (isTRUE(.data$filtered_rows)) {
# TRUE is default (i.e. no filter yet), so we don't need to & with it
.data$filtered_rows <- new_filter
} else {
.data$filtered_rows <- .data$filtered_rows & new_filter
}
}
.data
}
|
