cli progress bar benchmark

Introduction

We make sure that the timer is not TRUE, by setting it to ten hours.

library(cli)
# 10 hours
cli:::cli_tick_set(10 * 60 * 60 * 1000)
cli_tick_reset()
#> NULL
`__cli_update_due`
#> [1] FALSE

R benchmarks

The timer

fun <- function() NULL
ben_st <- bench::mark(
  `__cli_update_due`,
  fun(),
  .Call(ccli_tick_reset),
  interactive(),
  check = FALSE
)
ben_st
#> # A tibble: 4 × 6
#>   expression                  min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>             <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 __cli_update_due         7.92ns     12ns 76461204.        0B        0
#> 2 fun()                   89.06ns  205.9ns  3491271.        0B        0
#> 3 .Call(ccli_tick_reset)  83.94ns   93.9ns  8243099.        0B        0
#> 4 interactive()           13.97ns     20ns 38529251.        0B        0

ben_st2 <- bench::mark(
  if (`__cli_update_due`) foobar()
)
ben_st2
#> # A tibble: 1 × 6
#>   expression                            min  median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>                       <bch:tm> <bch:t>     <dbl> <bch:byt>    <dbl>
#> 1 if (`__cli_update_due`) foobar()     25ns  93.9ns 12148926.        0B        0

`cli_progress_along()`

seq <- 1:100000
ta <- cli_progress_along(seq)
bench::mark(seq[[1]], ta[[1]])
#> # A tibble: 2 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 seq[[1]]       95ns    105ns  7094556.        0B        0
#> 2 ta[[1]]       105ns    114ns  7423258.        0B        0

`for` loop

This is the baseline:

f0 <- function(n = 1e5) {
  x <- 0
  seq <- 1:n
  for (i in seq) {
    x <- x + i %% 2
  }
  x
}

With progress bars:

fp <- function(n = 1e5) {
  x <- 0
  seq <- 1:n
  for (i in cli_progress_along(seq)) {
    x <- x + seq[[i]] %% 2
  }
  x
}

Overhead per iteration:

ben_taf <- bench::mark(f0(), fp())
ben_taf
#> # A tibble: 2 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0()         8.54ms   8.91ms     110.     21.6KB     483.
#> 2 fp()         9.87ms  10.48ms      96.2    82.3KB     454.
(ben_taf$median[2] - ben_taf$median[1]) / 1e5
#> [1] 15.7ns

ben_taf2 <- bench::mark(f0(1e6), fp(1e6))
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
ben_taf2
#> # A tibble: 2 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0(1e+06)     109ms    143ms      6.59        0B     54.4
#> 2 fp(1e+06)     115ms    117ms      8.50    1.88KB     69.7
(ben_taf2$median[2] - ben_taf2$median[1]) / 1e6
#> [1] 1ns

ben_taf3 <- bench::mark(f0(1e7), fp(1e7))
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
ben_taf3
#> # A tibble: 2 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0(1e+07)     1.16s    1.16s     0.864        0B     71.7
#> 2 fp(1e+07)     1.36s    1.36s     0.733    1.88KB     60.8
(ben_taf3$median[2] - ben_taf3$median[1]) / 1e7
#> [1] 20.7ns

ben_taf4 <- bench::mark(f0(1e8), fp(1e8))
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
ben_taf4
#> # A tibble: 2 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0(1e+08)     11.5s    11.5s    0.0871        0B     33.7
#> 2 fp(1e+08)     12.1s    12.1s    0.0825    1.88KB     29.9
(ben_taf4$median[2] - ben_taf4$median[1]) / 1e8
#> [1] 6.41ns

Mapping with `lapply()`

This is the baseline:

f0 <- function(n = 1e5) {
  seq <- 1:n
  ret <- lapply(seq, function(x) {
    x %% 2
  })
  invisible(ret)
}

With an index vector:

f01 <- function(n = 1e5) {
  seq <- 1:n
  ret <- lapply(seq_along(seq), function(i) {
    seq[[i]] %% 2
  })
  invisible(ret)
}

With progress bars:

fp <- function(n = 1e5) {
  seq <- 1:n
  ret <- lapply(cli_progress_along(seq), function(i) {
    seq[[i]] %% 2
  })
  invisible(ret)
}

Overhead per iteration:

ben_tam <- bench::mark(f0(), f01(), fp())
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
ben_tam
#> # A tibble: 3 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0()         64.7ms   73.5ms     13.0      781KB    20.4 
#> 2 f01()       111.4ms    117ms      8.19     781KB    14.7 
#> 3 fp()        113.3ms    161ms      5.55     783KB     8.32
(ben_tam$median[3] - ben_tam$median[1]) / 1e5
#> [1] 875ns

ben_tam2 <- bench::mark(f0(1e6), f01(1e6), fp(1e6))
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
ben_tam2
#> # A tibble: 3 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0(1e+06)  742.79ms 742.79ms     1.35     7.63MB     8.08
#> 2 f01(1e+06)    1.27s    1.27s     0.789    7.63MB     6.31
#> 3 fp(1e+06)     1.44s    1.44s     0.693    7.63MB     4.85
(ben_tam2$median[3] - ben_tam2$median[1]) / 1e6
#> [1] 701ns
(ben_tam2$median[3] - ben_tam2$median[2]) / 1e6
#> [1] 177ns

Mapping with purrr

This is the baseline:

f0 <- function(n = 1e5) {
  seq <- 1:n
  ret <- purrr::map(seq, function(x) {
    x %% 2
  })
  invisible(ret)
}

With index vector:

f01 <- function(n = 1e5) {
  seq <- 1:n
  ret <- purrr::map(seq_along(seq), function(i) {
    seq[[i]] %% 2
  })
  invisible(ret)
}

With progress bars:

fp <- function(n = 1e5) {
  seq <- 1:n
  ret <- purrr::map(cli_progress_along(seq), function(i) {
    seq[[i]] %% 2
  })
  invisible(ret)
}

Overhead per iteration:

ben_pur <- bench::mark(f0(), f01(), fp())
ben_pur
#> # A tibble: 3 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0()         53.4ms   56.7ms      16.7    1.39MB     9.99
#> 2 f01()        66.6ms   74.4ms      13.6   781.3KB     5.43
#> 3 fp()         69.1ms   74.7ms      13.5  783.24KB    10.1
(ben_pur$median[3] - ben_pur$median[1]) / 1e5
#> [1] 180ns
(ben_pur$median[3] - ben_pur$median[2]) / 1e5
#> [1] 2.98ns

ben_pur2 <- bench::mark(f0(1e6), f01(1e6), fp(1e6))
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
ben_pur2
#> # A tibble: 3 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0(1e+06)  772.25ms 772.25ms     1.29     7.63MB     3.88
#> 2 f01(1e+06)       1s       1s     0.996    7.63MB     3.98
#> 3 fp(1e+06)     1.35s    1.35s     0.738    7.63MB     2.95
(ben_pur2$median[3] - ben_pur2$median[1]) / 1e6
#> [1] 583ns
(ben_pur2$median[3] - ben_pur2$median[2]) / 1e6
#> [1] 351ns

`ticking()`

f0 <- function(n = 1e5) {
  i <- 0
  x <- 0 
  while (i < n) {
    x <- x + i %% 2
    i <- i + 1
  }
  x
}

fp <- function(n = 1e5) {
  i <- 0
  x <- 0 
  while (ticking(i < n)) {
    x <- x + i %% 2
    i <- i + 1
  }
  x
}

ben_tk <- bench::mark(f0(), fp())
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
ben_tk
#> # A tibble: 2 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0()        10.46ms  17.58ms    55.9      39.3KB     3.99
#> 2 fp()          5.12s    5.12s     0.195   100.4KB     2.34
(ben_tk$median[2] - ben_tk$median[1]) / 1e5
#> [1] 51µs

Traditional API

f0 <- function(n = 1e5) {
  x <- 0
  for (i in 1:n) {
    x <- x + i %% 2
  }
  x
}

fp <- function(n = 1e5) {
  cli_progress_bar(total = n)
  x <- 0
  for (i in 1:n) {
    x <- x + i %% 2
    cli_progress_update()
  }
  x
}

ff <- function(n = 1e5) {
  cli_progress_bar(total = n)
  x <- 0
  for (i in 1:n) {
    x <- x + i %% 2
    if (`__cli_update_due`) cli_progress_update()
  }
  x
}

ben_api <- bench::mark(f0(), ff(), fp())
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
ben_api
#> # A tibble: 3 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0()         8.98ms   9.74ms    85.0      18.7KB     9.88
#> 2 ff()        15.62ms  16.45ms    55.9      27.6KB     5.99
#> 3 fp()          1.84s    1.84s     0.544    25.1KB     3.26
(ben_api$median[3] - ben_api$median[1]) / 1e5
#> [1] 18.3µs
(ben_api$median[2] - ben_api$median[1]) / 1e5
#> [1] 67.1ns

ben_api2 <- bench::mark(f0(1e6), ff(1e6), fp(1e6))
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
ben_api2
#> # A tibble: 3 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0(1e+06)   100.3ms  101.5ms    9.63          0B    11.6 
#> 2 ff(1e+06)   158.9ms    177ms    5.83       1.9KB     5.83
#> 3 fp(1e+06)     20.5s    20.5s    0.0487     1.9KB     2.92
(ben_api2$median[3] - ben_api2$median[1]) / 1e6
#> [1] 20.4µs
(ben_api2$median[2] - ben_api2$median[1]) / 1e6
#> [1] 75.5ns

C benchmarks

Baseline function:

SEXP test_baseline() {
  int i;
  int res = 0;
  for (i = 0; i < 2000000000; i++) {
    res += i % 2;
  }
  return ScalarInteger(res);
}

Switch + modulo check:

SEXP test_modulo(SEXP progress) {
  int i;
  int res = 0;
  int progress_ = LOGICAL(progress)[0];
  for (i = 0; i < 2000000000; i++) {
    if (i % 10000 == 0 && progress_) cli_progress_set(R_NilValue, i);
    res += i % 2;
  }
  return ScalarInteger(res);
}

cli progress bar API:

SEXP test_cli() {
  int i;
  int res = 0;
  SEXP bar = PROTECT(cli_progress_bar(2000000000, NULL));
  for (i = 0; i < 2000000000; i++) {
    if (CLI_SHOULD_TICK) cli_progress_set(bar, i);
    res += i % 2;
  }
  cli_progress_done(bar);
  UNPROTECT(1);
  return ScalarInteger(res);
}

SEXP test_cli_unroll() {
  int i = 0;
  int res = 0;
  SEXP bar = PROTECT(cli_progress_bar(2000000000, NULL));
  int s, final, step = 2000000000 / 100000;
  for (s = 0; s < 100000; s++) {
    if (CLI_SHOULD_TICK) cli_progress_set(bar, i);
    final = (s + 1) * step;
    for (i = s * step; i < final; i++) {
      res += i % 2;
    }
  }
  cli_progress_done(bar);
  UNPROTECT(1);
  return ScalarInteger(res);
}

library(progresstest)
ben_c <- bench::mark(
  test_baseline(),
  test_modulo(),
  test_cli(),
  test_cli_unroll()
)
ben_c
#> # A tibble: 4 × 6
#>   expression             min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>        <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 test_baseline()   801.26ms 801.26ms     1.25     2.08KB        0
#> 2 test_modulo()        1.17s    1.17s     0.853    2.23KB        0
#> 3 test_cli()           1.14s    1.14s     0.879  174.97KB        0
#> 4 test_cli_unroll() 807.85ms 807.85ms     1.24     3.48KB        0
(ben_c$median[3] - ben_c$median[1]) / 2000000000
#> [1] 1ns

Display update

We only update the display a fixed number of times per second. (Currently maximum five times per second.)

Let’s measure how long a single update takes.

Iterator with a bar

cli_progress_bar(total = 100000)
bench::mark(cli_progress_update(force = TRUE), max_iterations = 10000)
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#> # A tibble: 1 × 6
#>   expression                             min median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>                        <bch:tm> <bch:>     <dbl> <bch:byt>    <dbl>
#> 1 cli_progress_update(force = TRUE)   5.26ms 5.72ms      171.    1.43MB     21.1
cli_progress_done()

Iterator without a bar

cli_progress_bar(total = NA)
bench::mark(cli_progress_update(force = TRUE), max_iterations = 10000)
#> ⠙ 1 done (31/s) | 33ms
#> ⠹ 2 done (33/s) | 62ms
#> ⠸ 3 done (44/s) | 68ms
#> ⠼ 4 done (54/s) | 75ms
#> ⠴ 5 done (62/s) | 82ms
#> ⠦ 6 done (69/s) | 88ms
#> ⠧ 7 done (72/s) | 98ms
#> ⠇ 8 done (77/s) | 105ms
#> ⠏ 9 done (82/s) | 111ms
#> ⠋ 10 done (86/s) | 117ms
#> ⠙ 11 done (89/s) | 124ms
#> ⠹ 12 done (92/s) | 131ms
#> ⠸ 13 done (95/s) | 137ms
#> ⠼ 14 done (98/s) | 144ms
#> ⠴ 15 done (98/s) | 154ms
#> ⠦ 16 done (100/s) | 160ms
#> ⠧ 17 done (102/s) | 167ms
#> ⠇ 18 done (104/s) | 173ms
#> ⠏ 19 done (106/s) | 180ms
#> ⠋ 20 done (108/s) | 186ms
#> ⠙ 21 done (109/s) | 193ms
#> ⠹ 22 done (109/s) | 203ms
#> ⠸ 23 done (110/s) | 209ms
#> ⠼ 24 done (112/s) | 216ms
#> ⠴ 25 done (113/s) | 222ms
#> ⠦ 26 done (114/s) | 229ms
#> ⠧ 27 done (115/s) | 236ms
#> ⠇ 28 done (116/s) | 242ms
#> ⠏ 29 done (117/s) | 249ms
#> ⠋ 30 done (116/s) | 259ms
#> ⠙ 31 done (117/s) | 265ms
#> ⠹ 32 done (118/s) | 272ms
#> ⠸ 33 done (119/s) | 278ms
#> ⠼ 34 done (120/s) | 285ms
#> ⠴ 35 done (120/s) | 291ms
#> ⠦ 36 done (121/s) | 298ms
#> ⠧ 37 done (120/s) | 308ms
#> ⠇ 38 done (121/s) | 314ms
#> ⠏ 39 done (122/s) | 321ms
#> ⠋ 40 done (122/s) | 328ms
#> ⠙ 41 done (123/s) | 334ms
#> ⠹ 42 done (124/s) | 340ms
#> ⠸ 43 done (124/s) | 347ms
#> ⠼ 44 done (124/s) | 357ms
#> ⠴ 45 done (124/s) | 363ms
#> ⠦ 46 done (125/s) | 369ms
#> ⠧ 47 done (125/s) | 376ms
#> ⠇ 48 done (126/s) | 382ms
#> ⠏ 49 done (126/s) | 389ms
#> ⠋ 50 done (127/s) | 396ms
#> ⠙ 51 done (127/s) | 402ms
#> ⠹ 52 done (126/s) | 412ms
#> ⠸ 53 done (127/s) | 419ms
#> ⠼ 54 done (127/s) | 425ms
#> ⠴ 55 done (128/s) | 432ms
#> ⠦ 56 done (128/s) | 438ms
#> ⠧ 57 done (128/s) | 445ms
#> ⠇ 58 done (129/s) | 451ms
#> ⠏ 59 done (128/s) | 461ms
#> ⠋ 60 done (128/s) | 468ms
#> ⠙ 61 done (129/s) | 474ms
#> ⠹ 62 done (129/s) | 480ms
#> ⠸ 63 done (130/s) | 487ms
#> ⠼ 64 done (130/s) | 493ms
#> ⠴ 65 done (130/s) | 500ms
#> ⠦ 66 done (130/s) | 509ms
#> ⠧ 67 done (130/s) | 515ms
#> ⠇ 68 done (130/s) | 522ms
#> ⠏ 69 done (131/s) | 528ms
#> ⠋ 70 done (131/s) | 535ms
#> ⠙ 71 done (131/s) | 541ms
#> ⠹ 72 done (132/s) | 548ms
#> ⠸ 73 done (132/s) | 556ms
#> # A tibble: 1 × 6
#>   expression                             min median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>                        <bch:tm> <bch:>     <dbl> <bch:byt>    <dbl>
#> 1 cli_progress_update(force = TRUE)   6.03ms  6.5ms      153.     278KB     24.7
cli_progress_done()

Gábor Csárdi

2025-05-08

Introduction

R benchmarks

The timer

`cli_progress_along()`

`for` loop

Mapping with `lapply()`

Mapping with purrr

`ticking()`

Traditional API

C benchmarks

Display update

Iterator with a bar

Iterator without a bar

cli progress bar benchmark

Gábor Csárdi

2025-05-08

Introduction

R benchmarks

The timer

cli_progress_along()

for loop

Mapping with lapply()

Mapping with purrr

ticking()

Traditional API

C benchmarks

Display update

Iterator with a bar

Iterator without a bar

`cli_progress_along()`

`for` loop

Mapping with `lapply()`

`ticking()`