Install

bun add mitata

npm install mitata

try mitata in browser with ai assistant at https://bolt.new/~/mitata

Recommendations

• use dedicated hardware for running benchmarks
• run with garbage collection enabled (e.g. node --expose-gc ...)
• make sure your runtime has high-resolution timers and other relevant options/permissions enabled

Quick Start

import { run, bench, boxplot } from 'mitata';

function fibonacci(n) {
  if (n <= 1) return n;
  return fibonacci(n - 1) + fibonacci(n - 2);
}

bench('fibonacci(40)', () => fibonacci(40));

boxplot(() => {
  bench('new Array($size)', function* (state) {
    const size = state.get('size');
    yield () => Array.from({ length: size });
  }).range('size', 1, 1024);
});

await run();

#include "src/mitata.hpp"

int fibonacci(int n) {
  if (n <= 1) return n;
  return fibonacci(n - 1) + fibonacci(n - 2);
}

int main() {
  mitata::runner runner;
  runner.bench("noop", []() { });

  runner.summary([&]() {
    runner.bench("empty fn", []() { });
    runner.bench("fibonacci", []() { fibonacci(20); });
  });

  auto stats = runner.run();
}

configure your experience

import { run } from 'mitata';

await run({ format: 'mitata', colors: false }); // default format
await run({ filter: /new Array.*/ }) // only run benchmarks that match regex filter
await run({ throw: true }); // will immediately throw instead of handling error quietly

// c++
auto stats = runner.run({ .colors = true, .format = "json", .filter = std::regex(".*") });

automatic garbage collection

On runtimes that expose gc (e.g. bun, node --expose-gc ...), mitata will automatically run garbage collection before each benchmark.

This behavior can be further customized via the gc function on each benchmark (you should only do this when absolutely necessary - big gc spikes):

bench('lots of allocations', () => {
  Array.from({ length: 1024 }, () => Array.from({ length: 1024 }, () => new Array(1024)));
})
// false | 'once' (default) | 'inner'
// once runs gc after warmup
// inner runs gc after warmup and before each (batch-)iteration
.gc('inner');

universal compatibility

Out of box mitata can detect engine/runtime it's running on and fall back to using alternative non-standard I/O functions. If your engine or runtime is missing support, open an issue or pr requesting for support.

how to use mitata with engine CLIs like d8, jsc, graaljs, spidermonkey

$ xs bench.mjs
$ quickjs bench.mjs
$ d8 --expose-gc bench.mjs
$ spidermonkey -m bench.mjs
$ graaljs --js.timer-resolution=1 bench.mjs
$ /System/Library/Frameworks/JavaScriptCore.framework/Versions/Current/Helpers/jsc bench.mjs

// bench.mjs

import { print } from './src/lib.mjs';
import { run, bench } from './src/main.mjs'; // git clone
import { run, bench } from './node_modules/mitata/src/main.mjs'; // npm install

print('hello world'); // works on every engine

argumentizing your benchmarks has never been so easy

With other benchmarking libraries, often it's quite hard to easily make benchmarks that go over a range or run the same function with different arguments without writing spaghetti code, but now with mitata converting your benchmark to use arguments is just a function call away.

import { bench } from 'mitata';

bench(function* look_mom_no_spaghetti(state) {
  const len = state.get('len');
  const len2 = state.get('len2');
  yield () => new Array(len * len2);
})

.args('len', [1, 2, 3])
.range('len', 1, 1024) // 1, 8, 64, 512...
.dense_range('len', 1, 100) // 1, 2, 3 ... 99, 100
.args({ len: [1, 2, 3], len2: ['4', '5', '6'] }) // every possible combination

helpful warnings

For those who love doing micro-benchmarks, mitata can automatically detect and inform you about optimization passes like dead code elimination without requiring any special engine flags.

-------------------------------------- -------------------------------
1 + 1                   318.63 ps/iter 325.37 ps        ▇  █           !
                (267.92 ps … 14.28 ns) 382.81 ps ▁▁▁▁▁▁▁█▁▁█▁▁▁▁▁▁▁▁▁▁
empty function          319.36 ps/iter 325.37 ps          █ ▅          !
                (248.62 ps … 46.61 ns) 382.81 ps ▁▁▁▁▁▁▃▁▁█▁█▇▁▁▁▁▁▁▁▁

! = benchmark was likely optimized out (dead code elimination)

powerful visualizations right in your terminal

with mitata’s ascii rendering capabilities, now you can easily visualize samples in barplots, boxplots, lineplots, histograms, and get clear summaries without any additional tools or dependencies.

-------------------------------------- -------------------------------
1 + 1                   318.11 ps/iter 325.37 ps         ▇   █         !
                (267.92 ps … 11.14 ns) 363.97 ps ▁▁▁▁▁▁▁▁█▁▁▁█▁▁▁▁▁▁▁▁
Date.now()               27.69 ns/iter  27.48 ns  █                   
                 (27.17 ns … 44.10 ns)  32.74 ns ▃█▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁

                        ┌                                            ┐
                  1 + 1 ┤■ 318.11 ps 
             Date.now() ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 27.69 ns 
                        └                                            ┘

-------------------------------------- -------------------------------
Bubble Sort               2.11 ms/iter   2.26 ms  █                   
                   (1.78 ms … 6.93 ms)   4.77 ms ▃█▃▆▅▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
Quick Sort              159.60 µs/iter 154.50 µs  █                   
               (133.13 µs … 792.21 µs) 573.00 µs ▅█▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
Native Sort              97.20 µs/iter  97.46 µs        ██            
                (90.88 µs … 688.92 µs) 105.00 µs ▁▁▂▁▁▂▇██▇▃▃▃▃▃▂▂▂▁▁▁

                        ┌                                            ┐
                                        ╷┌─┬─┐                       ╷
            Bubble Sort                 ├┤ │ ├───────────────────────┤
                                        ╵└─┴─┘                       ╵
                        ┬   ╷
             Quick Sort │───┤
                        ┴   ╵
                        ┬
            Native Sort │
                        ┴
                        └                                            ┘
                        90.88 µs            2.43 ms            4.77 ms

-------------------------------------- -------------------------------
new Array(1)              3.57 ns/iter   3.20 ns    6.64 ns ▁█▄▂▁▁▁▁▁▁
new Array(8)              5.21 ns/iter   4.31 ns    8.85 ns ▁█▄▁▁▁▁▁▁▁
new Array(64)            17.94 ns/iter  13.40 ns  171.89 ns █▂▁▁▁▁▁▁▁▁
new Array(512)          188.05 ns/iter 246.88 ns  441.81 ns █▃▃▃▃▂▂▁▁▁
new Array(1024)         364.93 ns/iter 466.91 ns  600.34 ns █▄▁▁▁▅▅▃▂▁
Array.from(1)            29.73 ns/iter  29.24 ns   36.88 ns ▁█▄▃▂▁▁▁▁▁
Array.from(8)            33.96 ns/iter  32.99 ns   42.45 ns ▂█▄▂▂▁▁▁▁▁
Array.from(64)          146.52 ns/iter 143.82 ns  310.93 ns █▅▁▁▁▁▁▁▁▁
Array.from(512)           1.11 µs/iter   1.18 µs    1.34 µs ▃▅█▂▆▅▄▂▂▁
Array.from(1024)          1.98 µs/iter   2.09 µs    2.40 µs ▃█▃▃▇▇▄▂▁▁

summary
  new Array($len)
   5.42…8.33x faster than Array.from($len)

                        ┌                                            ┐
      Array.from($size)                                            ⢠⠊
       new Array($size)                                          ⢀⠔⠁ 
                                                                ⡠⠃   
                                                              ⢀⠎     
                                                             ⡔⠁      
                                                           ⡠⠊        
                                                         ⢀⠜          
                                                        ⡠⠃           
                                                       ⡔⠁            
                                                     ⢀⠎              
                                                    ⡠⠃               
                                                  ⢀⠜                 
                                                 ⢠⠊             ⣀⣀⠤⠤⠒
                                                ⡰⠁       ⣀⡠⠤⠔⠒⠊⠉     
                                           ⣀⣀⣀⠤⠜   ⣀⡠⠤⠒⠊⠉            
                         ⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣔⣒⣒⣊⣉⠭⠤⠤⠤⠤⠤⠒⠊⠉               
                        └                                            ┘

give your own code power of mitata

In case you don’t need all the fluff that comes with mitata or just need raw results, mitata exports its fundamental building blocks to allow you to easily build your own tooling and wrappers without losing any core benefits of using mitata.

#include "src/mitata.hpp"

int main() {
  auto stats = mitata::lib::fn([]() { /***/ })
}

import { B, measure } from 'mitata';

// lowest level for power users
const stats = await measure(function* (state) {
  const size = state.get('x');
  yield () => new Array(size);
}, {
  args: { x: 1 },
  batch_samples: 5 * 1024,
  min_cpu_time: 1000 * 1e6,
});

// explore how magic happens
console.log(stats.debug) // -> jit optimized source code of benchmark

// higher level api that includes mitata's argument and range features
const b = new B('new Array($x)', state => {
  const size = state.get('x');
  for (const _ of state) new Array(size);
}).args('x', [1, 5, 10]);

const trial = await b.run();

accuracy down to picoseconds

mitata pushes the limits of javascript with jit-generated zero-overhead measurement loops to provide high-resolution timings. This allows providing features like cpu clock frequency and dead code detection without requiring access outside the js sandbox.

clk: ~3.13 GHz
cpu: Apple M2 Pro
runtime: node 22.8.0 (arm64-darwin)

benchmark              avg (min … max) p75   p99    (min … top 1%)
-------------------------------------- -------------------------------
noop                     93.09 ps/iter  91.55 ps                █      !
                 (61.04 ps … 20.30 ns) 101.81 ps ▁▁▁▁▁▁▁▁▁▁▂▁▁▁▁█▁▁▁▁▂

! = benchmark was likely optimized out (dead code elimination)

// vs other libraries

16041.00 ns/iter - node:perf_hooks (performance.timerify)

5.30 ns/iter - https://npmjs.com/benchmark
noop x 188,640,251 ops/sec ±5.71% (73 runs sampled)

36.62 ns/iter - https://npmjs.com/tinybench
┌─────────┬───────────┬──────────────┬───────────────────┬──────────┬──────────┐
│ (index) │ Task Name │ ops/sec      │ Average Time (ns) │ Margin   │ Samples  │
├─────────┼───────────┼──────────────┼───────────────────┼──────────┼──────────┤
│ 0       │ 'noop'    │ '27,308,739' │ 36.61831406333669 │ '±0.14%' │ 13654370 │
└─────────┴───────────┴──────────────┴───────────────────┴──────────┴──────────┘

156.5685 ns/iter - https://npmjs.com/cronometro
╔══════════════╤═════════╤═══════════════════╤═══════════╗
║ Slower tests │ Samples │            Result │ Tolerance ║
╟──────────────┼─────────┼───────────────────┼───────────╢
║ Fastest test │ Samples │            Result │ Tolerance ║
╟──────────────┼─────────┼───────────────────┼───────────╢
║ noop         │   10000 │ 6386980.78 op/sec │  ± 1.85 % ║
╚══════════════╧═════════╧═══════════════════╧═══════════╝

clk: ~0.06 GHz
cpu: Apple M2 Pro
runtime: node 22.8.0 (arm64-darwin)

benchmark              avg (min … max) p75   p99    (min … top 1%)
-------------------------------------- -------------------------------
noop                     14.69 ns/iter  15.09 ns      ▃▅▇▇▇█▅▄▂        !
                 (13.33 ns … 19.69 ns)  16.24 ns ▁▄▅▇███████████▆▅▄▃▂▂

! = benchmark was likely optimized out (dead code elimination)

// vs other libraries

17500.00 ns/iter - node:perf_hooks (performance.timerify)

23.28 ns/iter - https://npmjs.com/benchmark
noop x 42,952,144 ops/sec ±0.87% (98 runs sampled)

184.92 ns/iter - https://npmjs.com/tinybench
┌─────────┬───────────┬─────────────┬────────────────────┬──────────┬─────────┐
│ (index) │ Task Name │ ops/sec     │ Average Time (ns)  │ Margin   │ Samples │
├─────────┼───────────┼─────────────┼────────────────────┼──────────┼─────────┤
│ 0       │ 'noop'    │ '5,407,742' │ 184.92003948393378 │ '±0.02%' │ 2703872 │
└─────────┴───────────┴─────────────┴────────────────────┴──────────┴─────────┘

659.9353333333333 ns/iter - https://npmjs.com/cronometro
╔══════════════╤═════════╤═══════════════════╤═══════════╗
║ Slower tests │ Samples │            Result │ Tolerance ║
╟──────────────┼─────────┼───────────────────┼───────────╢
║ Fastest test │ Samples │            Result │ Tolerance ║
╟──────────────┼─────────┼───────────────────┼───────────╢
║ noop         │    1500 │ 1515299.98 op/sec │  ± 0.72 % ║
╚══════════════╧═════════╧═══════════════════╧═══════════╝

License

MIT © Evan