Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

fs::path_dir is significantly slower on Windows vs Mac #424

Closed
orgadish opened this issue Jun 12, 2023 · 4 comments
Closed

fs::path_dir is significantly slower on Windows vs Mac #424

orgadish opened this issue Jun 12, 2023 · 4 comments

Comments

@orgadish
Copy link

When you use read_csv(..., id="file_path") you end up with a file_path column that has lots of repeats. Manipulations on this kind of column are somewhat slow. I have found that it's much faster to do a left_join(x, distinct(x, file_path) |> mutate(...), by="file_path") than it is to do the mutate directly.

Below I timed using fs::path_dir directly (with no tibble involvement at all) compared to going via a tibble to do the above. Even though the latter is significantly more indirect, it's more than 13X faster:

paths <- fs::path("base", stringr::str_glue("dir{d}", d=1:10), "inner") |> 
  rep(1e3)

bench::mark(
  direct = fs::path_dir(paths),
  indirect = {
    x <- tibble::tibble(a = paths)
    dplyr::left_join(x, dplyr::distinct(x, a) |> dplyr::mutate(b = fs::path_dir(a)), 
                     by = "a") |> 
      dplyr::pull(b)
  },
  iterations = 50
)
#> # A tibble: 2 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 direct      54.05ms  55.66ms      17.9  749.02KB    0.746
#> 2 indirect     3.68ms   4.14ms     237.     6.47MB    9.86

Created on 2023-06-12 with reprex v2.0.2

Session info 
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.3.0 (2023-04-21 ucrt)
#>  os       Windows 10 x64 (build 19045)
#>  system   x86_64, mingw32
#>  ui       RTerm
#>  language (EN)
#>  collate  English_United States.utf8
#>  ctype    English_United States.utf8
#>  tz       America/Los_Angeles
#>  date     2023-06-12
#>  pandoc   2.19.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  ! package     * version date (UTC) lib source
#>  P bench         1.1.3   2023-05-04 [?] CRAN (R 4.3.0)
#>    cli           3.6.1   2023-03-23 [1] CRAN (R 4.3.0)
#>    digest        0.6.31  2022-12-11 [1] CRAN (R 4.3.0)
#>    dplyr         1.1.2   2023-04-20 [1] CRAN (R 4.3.0)
#>    evaluate      0.21    2023-05-05 [1] CRAN (R 4.3.0)
#>    fansi         1.0.4   2023-01-22 [1] CRAN (R 4.3.0)
#>    fastmap       1.1.1   2023-02-24 [1] CRAN (R 4.3.0)
#>    fs            1.6.2   2023-04-25 [1] CRAN (R 4.3.0)
#>    generics      0.1.3   2022-07-05 [1] CRAN (R 4.3.0)
#>    glue          1.6.2   2022-02-24 [1] CRAN (R 4.3.0)
#>    htmltools     0.5.5   2023-03-23 [1] CRAN (R 4.3.0)
#>    knitr         1.42    2023-01-25 [1] CRAN (R 4.3.0)
#>    lifecycle     1.0.3   2022-10-07 [1] CRAN (R 4.3.0)
#>    magrittr      2.0.3   2022-03-30 [1] CRAN (R 4.3.0)
#>    pillar        1.9.0   2023-03-22 [1] CRAN (R 4.3.0)
#>    pkgconfig     2.0.3   2019-09-22 [1] CRAN (R 4.3.0)
#>  P profmem       0.6.0   2020-12-13 [?] CRAN (R 4.3.0)
#>    R6            2.5.1   2021-08-19 [1] CRAN (R 4.3.0)
#>    reprex        2.0.2   2022-08-17 [1] CRAN (R 4.3.0)
#>    rlang         1.1.1   2023-04-28 [1] CRAN (R 4.3.0)
#>    rmarkdown     2.21    2023-03-26 [1] CRAN (R 4.3.0)
#>    rstudioapi    0.14    2022-08-22 [1] CRAN (R 4.3.0)
#>    sessioninfo   1.2.2   2021-12-06 [1] CRAN (R 4.3.0)
#>    stringi       1.7.12  2023-01-11 [1] CRAN (R 4.3.0)
#>    stringr       1.5.0   2022-12-02 [1] CRAN (R 4.3.0)
#>    tibble        3.2.1   2023-03-20 [1] CRAN (R 4.3.0)
#>    tidyselect    1.2.0   2022-10-10 [1] CRAN (R 4.3.0)
#>    utf8          1.2.3   2023-01-31 [1] CRAN (R 4.3.0)
#>    vctrs         0.6.2   2023-04-19 [1] CRAN (R 4.3.0)
#>    withr         2.5.0   2022-03-03 [1] CRAN (R 4.3.0)
#>    xfun          0.39    2023-04-20 [1] CRAN (R 4.3.0)
#>    yaml          2.3.7   2023-01-23 [1] CRAN (R 4.3.0)
#> 
#>  [1] C:/Users/LAB/Desktop/2023-05 Sequence Recovery Analysis/renv/library/R-4.3/x86_64-w64-mingw32
#>  [2] C:/Users/LAB/AppData/Local/R/cache/R/renv/sandbox/R-4.3/x86_64-w64-mingw32/830ce55b
#>  [3] C:/Program Files/R/R-4.3.0/library
#> 
#>  P ── Loaded and on-disk path mismatch.
#> 
#> ──────────────────────────────────────────────────────────────────────────────
@orgadish
Copy link
Author

orgadish commented Jun 12, 2023 •
edited
Loading

Obviously, even faster without going through a tibble at all. The approach below (using func(unique(x))[match(x, unique(x)]) might be a reasonable approach overall as it doesn't seem to add significant overhead compared to applying directly, even if all paths are already unique.

fast_fs <- function(x, func) {
  ux <- unique(x)
  uy <- func(ux)
  uy[match(x, ux)]
}

repeated_paths <- fs::path("base", stringr::str_glue("dir{d}", d=1:10), "inner") |> 
  rep(1e3)

bench::mark(
  direct = repeated_paths |> fs::path_dir(),
  indirect = repeated_paths |> fast_fs(fs::path_dir),
  iterations = 50
)
#> # A tibble: 2 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 direct       54.1ms   56.3ms      17.7     749KB    0.361
#> 2 indirect    283.6µs  395.8µs    2242.      365KB    0

unique_paths <- fs::path("base", stringr::str_glue("dir{d}", d=1:1e4), "inner")

bench::mark(
  direct = unique_paths |> fs::path_dir(),
  indirect = unique_paths |> fast_fs(fs::path_dir),
  iterations = 50
)
#> # A tibble: 2 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 direct       56.1ms   57.1ms      17.4  901.88KB    0.726
#> 2 indirect     56.4ms   57.5ms      17.3    1.36MB    0.723

Created on 2023-06-12 with reprex v2.0.2

Session info 
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.3.0 (2023-04-21 ucrt)
#>  os       Windows 10 x64 (build 19045)
#>  system   x86_64, mingw32
#>  ui       RTerm
#>  language (EN)
#>  collate  English_United States.utf8
#>  ctype    English_United States.utf8
#>  tz       America/Los_Angeles
#>  date     2023-06-12
#>  pandoc   2.19.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  ! package     * version date (UTC) lib source
#>  P bench         1.1.3   2023-05-04 [?] CRAN (R 4.3.0)
#>    cli           3.6.1   2023-03-23 [1] CRAN (R 4.3.0)
#>    digest        0.6.31  2022-12-11 [1] CRAN (R 4.3.0)
#>    evaluate      0.21    2023-05-05 [1] CRAN (R 4.3.0)
#>    fansi         1.0.4   2023-01-22 [1] CRAN (R 4.3.0)
#>    fastmap       1.1.1   2023-02-24 [1] CRAN (R 4.3.0)
#>    fs            1.6.2   2023-04-25 [1] CRAN (R 4.3.0)
#>    glue          1.6.2   2022-02-24 [1] CRAN (R 4.3.0)
#>    htmltools     0.5.5   2023-03-23 [1] CRAN (R 4.3.0)
#>    knitr         1.42    2023-01-25 [1] CRAN (R 4.3.0)
#>    lifecycle     1.0.3   2022-10-07 [1] CRAN (R 4.3.0)
#>    magrittr      2.0.3   2022-03-30 [1] CRAN (R 4.3.0)
#>    pillar        1.9.0   2023-03-22 [1] CRAN (R 4.3.0)
#>    pkgconfig     2.0.3   2019-09-22 [1] CRAN (R 4.3.0)
#>  P profmem       0.6.0   2020-12-13 [?] CRAN (R 4.3.0)
#>    reprex        2.0.2   2022-08-17 [1] CRAN (R 4.3.0)
#>    rlang         1.1.1   2023-04-28 [1] CRAN (R 4.3.0)
#>    rmarkdown     2.21    2023-03-26 [1] CRAN (R 4.3.0)
#>    rstudioapi    0.14    2022-08-22 [1] CRAN (R 4.3.0)
#>    sessioninfo   1.2.2   2021-12-06 [1] CRAN (R 4.3.0)
#>    stringi       1.7.12  2023-01-11 [1] CRAN (R 4.3.0)
#>    stringr       1.5.0   2022-12-02 [1] CRAN (R 4.3.0)
#>    tibble        3.2.1   2023-03-20 [1] CRAN (R 4.3.0)
#>    utf8          1.2.3   2023-01-31 [1] CRAN (R 4.3.0)
#>    vctrs         0.6.2   2023-04-19 [1] CRAN (R 4.3.0)
#>    withr         2.5.0   2022-03-03 [1] CRAN (R 4.3.0)
#>    xfun          0.39    2023-04-20 [1] CRAN (R 4.3.0)
#>    yaml          2.3.7   2023-01-23 [1] CRAN (R 4.3.0)
#> 
#>  [1] C:/Users/LAB/Desktop/2023-05 Sequence Recovery Analysis/renv/library/R-4.3/x86_64-w64-mingw32
#>  [2] C:/Users/LAB/AppData/Local/R/cache/R/renv/sandbox/R-4.3/x86_64-w64-mingw32/830ce55b
#>  [3] C:/Program Files/R/R-4.3.0/library
#> 
#>  P ── Loaded and on-disk path mismatch.
#> 
#> ──────────────────────────────────────────────────────────────────────────────

@orgadish orgadish mentioned this issue Jul 6, 2023
Open
@orgadish
Copy link
Author

orgadish commented Jul 7, 2023

Interestingly, path_file and path_dir are 18x slower on Windows than on Mac (see testing in #425). Not sure if that's something that can/should be addressed rather than implementing deduplication to speed up.

@orgadish orgadish changed the title fs::path_dir is significantly slower than expected on vectors with repeats fs::path_dir is significantly slower on Windows vs Mac Jul 27, 2023
@wch wch mentioned this issue Jul 27, 2023
Closed
@orgadish orgadish mentioned this issue Jul 27, 2023
Closed
@orgadish
Copy link
Author

Given that these functions are essentially just wrappers around the equivalent base methods, it's unsurprising that the trend holds for those methods (see reprex below).

As such, I am closing this since this issue is not fs specific. I have submitted the bug by email to the Bugzilla moderators.

Mac Reprex

N <- 10000

generate_vec <- function(l) lapply(1:N, \(dummy) sample(LETTERS, l, replace=T) |> 
                                     paste0(collapse="/")) |>
  unlist()

vec3 <- generate_vec(3)
vec100 <- generate_vec(100)

bench::mark(
  tolower(vec3),
  tolower(vec100),
  check=FALSE,
  iterations=10
)
#> # A tibble: 2 × 6
#>   expression           min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>      <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 tolower(vec3)      2.9ms   3.08ms     319.    78.17KB        0
#> 2 tolower(vec100)   41.6ms  43.84ms      22.1    2.44MB        0

bench::mark(
  basename(vec3),
  basename(vec100),
  check=FALSE,
  iterations=10
)
#> # A tibble: 2 × 6
#>   expression            min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>       <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 basename(vec3)     1.67ms   1.86ms      535.    78.2KB        0
#> 2 basename(vec100)   3.01ms   3.13ms      302.    78.2KB        0

Created on 2023-09-15 with reprex v2.0.2

Session info 
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.2.2 (2022-10-31)
#>  os       macOS Big Sur ... 10.16
#>  system   x86_64, darwin17.0
#>  ui       X11
#>  language (EN)
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       America/New_York
#>  date     2023-09-15
#>  pandoc   3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package     * version date (UTC) lib source
#>  bench         1.1.2   2021-11-30 [1] CRAN (R 4.2.0)
#>  cli           3.6.1   2023-03-23 [1] CRAN (R 4.2.0)
#>  digest        0.6.31  2022-12-11 [1] CRAN (R 4.2.0)
#>  evaluate      0.19    2022-12-13 [1] CRAN (R 4.2.0)
#>  fansi         1.0.4   2023-01-22 [1] CRAN (R 4.2.0)
#>  fastmap       1.1.1   2023-02-24 [1] CRAN (R 4.2.0)
#>  fs            1.6.2   2023-04-25 [1] CRAN (R 4.2.0)
#>  glue          1.6.2   2022-02-24 [1] CRAN (R 4.2.0)
#>  highr         0.10    2022-12-22 [1] CRAN (R 4.2.0)
#>  htmltools     0.5.3   2022-07-18 [1] CRAN (R 4.2.0)
#>  knitr         1.41    2022-11-18 [1] CRAN (R 4.2.0)
#>  lifecycle     1.0.3   2022-10-07 [1] CRAN (R 4.2.0)
#>  magrittr      2.0.3   2022-03-30 [1] CRAN (R 4.2.0)
#>  pillar        1.9.0   2023-03-22 [1] CRAN (R 4.2.0)
#>  pkgconfig     2.0.3   2019-09-22 [1] CRAN (R 4.2.0)
#>  profmem       0.6.0   2020-12-13 [1] CRAN (R 4.2.0)
#>  purrr         1.0.1   2023-01-10 [1] CRAN (R 4.2.0)
#>  R.cache       0.16.0  2022-07-21 [1] CRAN (R 4.2.0)
#>  R.methodsS3   1.8.2   2022-06-13 [1] CRAN (R 4.2.0)
#>  R.oo          1.25.0  2022-06-12 [1] CRAN (R 4.2.0)
#>  R.utils       2.12.2  2022-11-11 [1] CRAN (R 4.2.0)
#>  reprex        2.0.2   2022-08-17 [1] CRAN (R 4.2.0)
#>  rlang         1.1.1   2023-04-28 [1] CRAN (R 4.2.0)
#>  rmarkdown     2.14    2022-04-25 [1] CRAN (R 4.2.0)
#>  rstudioapi    0.13    2020-11-12 [1] CRAN (R 4.2.0)
#>  sessioninfo   1.2.2   2021-12-06 [1] CRAN (R 4.2.0)
#>  stringi       1.7.12  2023-01-11 [1] CRAN (R 4.2.0)
#>  stringr       1.5.0   2022-12-02 [1] CRAN (R 4.2.0)
#>  styler        1.8.1   2022-11-07 [1] CRAN (R 4.2.0)
#>  tibble        3.2.1   2023-03-20 [1] CRAN (R 4.2.0)
#>  utf8          1.2.3   2023-01-31 [1] CRAN (R 4.2.0)
#>  vctrs         0.6.3   2023-06-14 [1] CRAN (R 4.2.0)
#>  withr         2.5.0   2022-03-03 [1] CRAN (R 4.2.0)
#>  xfun          0.40    2023-08-09 [1] CRAN (R 4.2.2)
#>  yaml          2.3.6   2022-10-18 [1] CRAN (R 4.2.0)
#> 
#>  [1] /Library/Frameworks/R.framework/Versions/4.2/Resources/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────

Windows Reprex

N <- 10000

generate_vec <- function(l) lapply(1:N, \(dummy) sample(LETTERS, l, replace=T) |> 
                                     paste0(collapse="/")) |>
  unlist()

vec3 <- generate_vec(3)
vec100 <- generate_vec(100)

bench::mark(
  tolower(vec3),
  tolower(vec100),
  check=FALSE,
  iterations=10
)
#> # A tibble: 2 × 6
#>   expression           min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>      <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 tolower(vec3)     5.54ms   6.06ms     165.    78.17KB        0
#> 2 tolower(vec100)  77.67ms  79.17ms      12.6    2.44MB        0

bench::mark(
  basename(vec3),
  basename(vec100),
  check=FALSE,
  iterations=10
)
#> # A tibble: 2 × 6
#>   expression            min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>       <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 basename(vec3)     52.1ms   53.2ms     18.7    78.17KB     0   
#> 2 basename(vec100)   99.6ms  100.7ms      9.89    4.42MB     1.10

Created on 2023-09-15 with reprex v2.0.2

Session info 
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.3.0 (2023-04-21 ucrt)
#>  os       Windows 10 x64 (build 19045)
#>  system   x86_64, mingw32
#>  ui       RTerm
#>  language (EN)
#>  collate  English_United States.utf8
#>  ctype    English_United States.utf8
#>  tz       America/Los_Angeles
#>  date     2023-09-15
#>  pandoc   2.19.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  ! package     * version date (UTC) lib source
#>  P bench         1.1.3   2023-05-04 [?] CRAN (R 4.3.0)
#>    cli           3.6.1   2023-03-23 [1] CRAN (R 4.3.0)
#>    digest        0.6.31  2022-12-11 [1] CRAN (R 4.3.0)
#>    evaluate      0.21    2023-05-05 [1] CRAN (R 4.3.0)
#>    fansi         1.0.4   2023-01-22 [1] CRAN (R 4.3.0)
#>    fastmap       1.1.1   2023-02-24 [1] CRAN (R 4.3.0)
#>    fs            1.6.2   2023-04-25 [1] CRAN (R 4.3.0)
#>    glue          1.6.2   2022-02-24 [1] CRAN (R 4.3.0)
#>    htmltools     0.5.5   2023-03-23 [1] CRAN (R 4.3.0)
#>    knitr         1.42    2023-01-25 [1] CRAN (R 4.3.0)
#>    lifecycle     1.0.3   2022-10-07 [1] CRAN (R 4.3.0)
#>    magrittr      2.0.3   2022-03-30 [1] CRAN (R 4.3.0)
#>    pillar        1.9.0   2023-03-22 [1] CRAN (R 4.3.0)
#>    pkgconfig     2.0.3   2019-09-22 [1] CRAN (R 4.3.0)
#>  P profmem       0.6.0   2020-12-13 [?] CRAN (R 4.3.0)
#>    purrr         1.0.1   2023-01-10 [1] CRAN (R 4.3.0)
#>    R.cache       0.16.0  2022-07-21 [1] CRAN (R 4.3.1)
#>    R.methodsS3   1.8.2   2022-06-13 [1] CRAN (R 4.3.0)
#>    R.oo          1.25.0  2022-06-12 [1] CRAN (R 4.3.0)
#>    R.utils       2.12.2  2022-11-11 [1] CRAN (R 4.3.1)
#>    reprex        2.0.2   2022-08-17 [1] CRAN (R 4.3.0)
#>    rlang         1.1.1   2023-04-28 [1] CRAN (R 4.3.0)
#>    rmarkdown     2.21    2023-03-26 [1] CRAN (R 4.3.0)
#>    rstudioapi    0.14    2022-08-22 [1] CRAN (R 4.3.0)
#>    sessioninfo   1.2.2   2021-12-06 [1] CRAN (R 4.3.0)
#>    styler        1.10.2  2023-08-29 [1] CRAN (R 4.3.1)
#>    tibble        3.2.1   2023-03-20 [1] CRAN (R 4.3.0)
#>    utf8          1.2.3   2023-01-31 [1] CRAN (R 4.3.0)
#>    vctrs         0.6.2   2023-04-19 [1] CRAN (R 4.3.0)
#>    withr         2.5.0   2022-03-03 [1] CRAN (R 4.3.0)
#>    xfun          0.39    2023-04-20 [1] CRAN (R 4.3.0)
#>    yaml          2.3.7   2023-01-23 [1] CRAN (R 4.3.0)
#> 
#>  [1] C:/Users/LAB/Desktop/2023-05 Sequence Recovery Analysis/renv/library/R-4.3/x86_64-w64-mingw32
#>  [2] C:/Users/LAB/AppData/Local/R/cache/R/renv/sandbox/R-4.3/x86_64-w64-mingw32/830ce55b
#>  [3] C:/Program Files/R/R-4.3.0/library
#> 
#>  P ── Loaded and on-disk path mismatch.
#> 
#> ──────────────────────────────────────────────────────────────────────────────

@orgadish orgadish closed this as not planned Won't fix, can't repro, duplicate, stale Sep 16, 2023
@orgadish
Copy link
Author

To anyone that comes across this and is looking for a solution, I've written a package to do the general case of fast_fs shown above:

# if(!requireNamespace("deduped")) install.packages("deduped")

library(deduped)

N_TOTAL <- 1e4
repeated_paths <- fs::path("base", stringr::str_glue("dir{d}", d=1:10), "inner") |> 
  rep(N_TOTAL/10) |> 
  sample()

bench::mark(
  direct = repeated_paths |> fs::path_dir(),
  indirect = repeated_paths |> deduped(fs::path_dir)(),
  iterations = 10
)
#> # A tibble: 2 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 direct       51.8ms   52.5ms      18.9  749.02KB     2.10
#> 2 indirect    206.3µs  213.5µs    4574.     6.13MB     0

all_unique_paths <- fs::path("base", stringr::str_glue("dir{d}", d=1:N_TOTAL), "inner")

bench::mark(
  direct = all_unique_paths |> fs::path_dir(),
  indirect = all_unique_paths |> deduped(fs::path_dir)(),
  iterations = 10
)
#> # A tibble: 2 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 direct       53.6ms   54.6ms      18.3  901.88KB     0   
#> 2 indirect     53.6ms   54.9ms      18.2    1.03MB     2.02

Created on 2023-10-26 with reprex v2.0.2

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Assignees
No one assigned
Labels
None yet
Projects
None yet
Milestone
No milestone
Development

No branches or pull requests
1 participant
@orgadish