One may wonder how fast is
tidyfst. Well, it depends. Generally, it is as fast as
data.table because it is backed by it, but it would spend extra
time on the generation of data.table codes. This extra time is marginal
on large (and even small) data sets.
Now let’s do a test to
compare the performance of tidyfst, data.table and
dplyr. In the vignette we’ll use a small data set. The example
was provided by the data.table package (https://h2oai.github.io/db-benchmark/) and tweaked here.
These tests are based on computation by groups.
First let’s load
the package and generate some data.
# load packages
library(tidyfst)
#> Thank you for using tidyfst!
#> To acknowledge our work, please cite the package:
#> Huang et al., (2020). tidyfst: Tidy Verbs for Fast Data Manipulation. Journal of Open Source Software, 5(52), 2388, https://doi.org/10.21105/joss.02388
library(data.table)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:data.table':
#>
#> between, first, last
#> The following objects are masked from 'package:tidyfst':
#>
#> between, cummean, nth
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(bench)
# generate the data
# if you have a HPC and want to try larger data sets, increase N
N = 1e4
K = 1e2
set.seed(2020)
cat(sprintf("Producing data of %s rows and %s K groups factors\n", N, K))
#> Producing data of 10000 rows and 100 K groups factors
DT = data.table(
id1 = sample(sprintf("id%03d",1:K), N, TRUE), # large groups (char)
id2 = sample(sprintf("id%03d",1:K), N, TRUE), # large groups (char)
id3 = sample(sprintf("id%010d",1:(N/K)), N, TRUE), # small groups (char)
id4 = sample(K, N, TRUE), # large groups (int)
id5 = sample(K, N, TRUE), # large groups (int)
id6 = sample(N/K, N, TRUE), # small groups (int)
v1 = sample(5, N, TRUE), # int in range [1,5]
v2 = sample(5, N, TRUE), # int in range [1,5]
v3 = round(runif(N,max=100),4) # numeric e.g. 23.5749
)
object_size(DT)
#> 527.7 KbThis data is rather small, the size is around 527 Kb. However, with the bench package, we could detect the difference by increasing iteration times. In this way, examples listed here could be implemented even on relatively low performance computers.
Here, we try to get median and standard deviation by groups.After dplyr v1.0.0, the regrouping feature could be confusing sometimes (comes with warning message). If you are using it, make sure they are in the right groups before grouped computation. In tidyfst and data.table, we have “by” parameter to specify the groups. Here we would not check if the results are equal, because dplyr will return a tibble class even when we input a data.table in the first place. The iteration time is 10 for each of the test below.
bench::mark(
data.table = DT[,.(median_v3 = median(v3),
sd_v3 = sd(v3)),
by = .(id4,id5)],
tidyfst = DT %>%
summarise_dt(
by = c("id4", "id5"),
median_v3 = median(v3),
sd_v3 = sd(v3)
),
dplyr = DT %>%
group_by(id4,id5,.drop = TRUE) %>%
summarise(median_v3 = median(v3),sd_v3 = sd(v3)),
check = FALSE,iterations = 10
) -> q1
#> `summarise()` has grouped output by 'id4'. You can override using the `.groups`
#> argument.
#> `summarise()` has grouped output by 'id4'. You can override using the `.groups`
#> argument.
#> `summarise()` has grouped output by 'id4'. You can override using the `.groups`
#> argument.
#> `summarise()` has grouped output by 'id4'. You can override using the `.groups`
#> argument.
#> `summarise()` has grouped output by 'id4'. You can override using the `.groups`
#> argument.
#> `summarise()` has grouped output by 'id4'. You can override using the `.groups`
#> argument.
#> `summarise()` has grouped output by 'id4'. You can override using the `.groups`
#> argument.
#> `summarise()` has grouped output by 'id4'. You can override using the `.groups`
#> argument.
#> `summarise()` has grouped output by 'id4'. You can override using the `.groups`
#> argument.
#> `summarise()` has grouped output by 'id4'. You can override using the `.groups`
#> argument.
#> `summarise()` has grouped output by 'id4'. You can override using the `.groups`
#> argument.
q1
#> # A tibble: 3 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 data.table 1.65ms 1.7ms 578. 2.37MB 0
#> 2 tidyfst 1.66ms 1.7ms 587. 656.64KB 0
#> 3 dplyr 204.27ms 211.9ms 4.70 5.41MB 15.5We could find that spent time of tidyfst and data.table are quite similar, but much less than dplyr.
This example performs quite similar to the above one. tidyfst might spend a tiny little more time and space on code translation than data.table, but still performs much better than dplyr.
bench::mark(
data.table =DT[,.(range_v1_v2 = max(v1) - min(v2)),by = id3],
tidyfst = DT %>% summarise_dt(
by = id3,
range_v1_v2 = max(v1) - min(v2)
),
dplyr = DT %>%
group_by(id3,.drop = TRUE) %>%
summarise(range_v1_v2 = max(v1) - min(v2)),
check = FALSE,iterations = 10
) -> q2
q2
#> # A tibble: 3 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 data.table 617.42µs 651.84µs 1461. 92.9KB 162.
#> 2 tidyfst 617.97µs 657.59µs 1517. 92.9KB 0
#> 3 dplyr 2.71ms 2.76ms 355. 468.7KB 0Here we’ll display a rather different test to show the flexibly in
tidyfst. In tidyfst, if your code writes more like
data.table, the codes could speed up. If you write it more like
dplyr, the codes might be more readable but slows down. In
tidyfst, there is in_dt function for you to write
data.table codes to gain speed when you meet a bottomneck.
In
the following example, we use the exact same syntax of
data.table in tidyfst::in_dt.
bench::mark(
data.table =DT[order(-v3),.(largest2_v3 = head(v3,2L)),by = id6],
tidyfst = DT %>%
in_dt(order(-v3),.(largest2_v3 = head(v3,2L)),by = id6),
dplyr = DT %>%
select(id6,largest2_v3 = v3) %>%
group_by(id6) %>%
slice_max(largest2_v3,n = 2,with_ties = FALSE),
check = FALSE,iterations = 10
) -> q3
q3
#> # A tibble: 3 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 data.table 1.75ms 1.82ms 537. 607.77KB 0
#> 2 tidyfst 1.97ms 2.22ms 461. 1.03MB 51.2
#> 3 dplyr 16.64ms 17.07ms 57.8 2.01MB 14.4To summarise multiple columns by group, tidyfst has designed
a function named summarise_vars, which is even more
convenient than the across function in dplyr. It
first choose the columns, then tell it what to do, and you can provide
the “by” parameter to operate by groups (optional).
bench::mark(
data.table =DT[,lapply(.SD,mean),by = id4,.SDcols = v1:v3],
tidyfst = DT %>%
summarise_vars(
v1:v3,
mean,
by = id4
),
dplyr = DT %>%
group_by(id4) %>%
summarise(across(v1:v3,mean)),
check = FALSE,iterations = 10
) -> q4
q4
#> # A tibble: 3 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 data.table 779.25µs 847.93µs 1160. 489KB 0
#> 2 tidyfst 2.8ms 3.04ms 331. 319KB 0
#> 3 dplyr 4.77ms 5.11ms 190. 551KB 21.1Take a look at the performance, tidyfst still lies between data.table and dplyr.
Now let’s try more groups, here we use all the id (id1~id6) as group,
and get the sum and count. Note that tidyfst is written in
data.table, so it do not use n() in dplyr
but .N in data.table to get counts by group.
bench::mark(
data.table =DT[,.(v3 = sum(v3),count = .N),by = id1:id6],
tidyfst = DT %>%
summarise_dt(
by = id1:id6,
v3 = sum(v3),
count = .N
),
dplyr = DT %>%
group_by(id1,id2,id3,id4,id5,id6) %>%
summarise(v3 = sum(v3),count = n()),
check = FALSE,iterations = 10
) -> q5
#> `summarise()` has grouped output by 'id1', 'id2', 'id3', 'id4', 'id5'. You can
#> override using the `.groups` argument.
#> `summarise()` has grouped output by 'id1', 'id2', 'id3', 'id4', 'id5'. You can
#> override using the `.groups` argument.
#> `summarise()` has grouped output by 'id1', 'id2', 'id3', 'id4', 'id5'. You can
#> override using the `.groups` argument.
#> `summarise()` has grouped output by 'id1', 'id2', 'id3', 'id4', 'id5'. You can
#> override using the `.groups` argument.
#> `summarise()` has grouped output by 'id1', 'id2', 'id3', 'id4', 'id5'. You can
#> override using the `.groups` argument.
#> `summarise()` has grouped output by 'id1', 'id2', 'id3', 'id4', 'id5'. You can
#> override using the `.groups` argument.
#> `summarise()` has grouped output by 'id1', 'id2', 'id3', 'id4', 'id5'. You can
#> override using the `.groups` argument.
#> `summarise()` has grouped output by 'id1', 'id2', 'id3', 'id4', 'id5'. You can
#> override using the `.groups` argument.
#> `summarise()` has grouped output by 'id1', 'id2', 'id3', 'id4', 'id5'. You can
#> override using the `.groups` argument.
#> `summarise()` has grouped output by 'id1', 'id2', 'id3', 'id4', 'id5'. You can
#> override using the `.groups` argument.
#> `summarise()` has grouped output by 'id1', 'id2', 'id3', 'id4', 'id5'. You can
#> override using the `.groups` argument.
q5
#> # A tibble: 3 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 data.table 2.16ms 2.31ms 427. 1.02MB 0
#> 2 tidyfst 2.49ms 2.52ms 395. 1.02MB 0
#> 3 dplyr 85.71ms 91.38ms 9.89 3MB 16.8While in a data set of ~0.5 Mb we find that the performance of tidyfst lies between data.table and dplyr, we could discover that the speed is much closer to data.table. In fact, if you try a much larger data set in a computer with large RAM and multiple cores, you’ll find that the performance of tidyfst sticks close to data.table. If you are interested and has a high-performance computer, try to generate a larger data set and test out. Moreover, while the dplyr user might find these data manipulation verbs friendly, the innate syntax of tidyfst is more like data.table, and could be a good companion of data.table for some frequently used complex tasks.
sessionInfo()
#> R version 4.4.2 (2024-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] bench_1.1.3 dplyr_1.1.4 data.table_1.16.2 tidyfst_1.8.1
#> [5] rmarkdown_2.29
#>
#> loaded via a namespace (and not attached):
#> [1] jsonlite_1.8.9 compiler_4.4.2 tidyselect_1.2.1 Rcpp_1.0.13-1
#> [5] stringr_1.5.1 parallel_4.4.2 jquerylib_0.1.4 yaml_2.3.10
#> [9] fastmap_1.2.0 R6_2.5.1 generics_0.1.3 knitr_1.49
#> [13] tibble_3.2.1 maketools_1.3.1 bslib_0.8.0 pillar_1.9.0
#> [17] rlang_1.1.4 utf8_1.2.4 cachem_1.1.0 stringi_1.8.4
#> [21] xfun_0.49 sass_0.4.9 sys_3.4.3 cli_3.6.3
#> [25] withr_3.0.2 magrittr_2.0.3 digest_0.6.37 fst_0.9.8
#> [29] lifecycle_1.0.4 vctrs_0.6.5 evaluate_1.0.1 glue_1.8.0
#> [33] buildtools_1.0.0 profmem_0.6.0 fansi_1.0.6 fstcore_0.9.18
#> [37] tools_4.4.2 pkgconfig_2.0.3 htmltools_0.5.8.1