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Installation

First install the plugin @codspeed/tinybench-plugin and tinybench (if not already installed):
The CodSpeed plugin now requires tinybench v4 and above. Note that tinybench v4 has dropped support for CommonJS modules, so ESM is now required.
npm install --save-dev tinybench @codspeed/tinybench-plugin

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Usage

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Creating benchmarks

Let’s create a fibonacci function and benchmark it with tinybench and the CodSpeed plugin:
benches/bench.ts
import { withCodSpeed } from "@codspeed/tinybench-plugin";
import { Bench } from "tinybench";

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

const bench = withCodSpeed(new Bench());

bench
  .add("fibonacci10", () => {
    fibonacci(10);
  })
  .add("fibonacci15", () => {
    fibonacci(15);
  });

await bench.run();
console.table(bench.table());
Here, a few things are happening:
  • We create a simple recursive fibonacci function.
  • We create a new Bench instance with CodSpeed support by using the withCodSpeed helper. This step is critical to enable CodSpeed on your benchmarks.
  • We add two benchmarks to the suite and launch it, benching our fibonacci function for 10 and 15.

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Testing the benchmarks locally

Now, we can run our benchmarks locally to make sure everything is working as expected:
TypeScript runnerTo run the .ts file directly, we recommend using esbuild-register. It allows running TypeScript & ESM files directly with Node.js.
npm install --save-dev esbuild-register
$ node -r esbuild/register benches/bench.ts
[CodSpeed] 2 benches detected but no instrumentation found
[CodSpeed] falling back to tinybench
┌─────────┬───────────────┬───────────────────┬──────────┐
│ (index) │   Task Name   │ Average Time (ns) │  Margin  │
├─────────┼───────────────┼───────────────────┼──────────┤
│    0    │ 'fibonacci10' │ 552.4139857896414 │ '±0.18%' │
│    1    │ 'fibonacci15' │ 5633.276191749634 │ '±0.14%' │
└─────────┴───────────────┴───────────────────┴──────────┘
And… Congrats🎉, CodSpeed is installed in your benchmarking suite! Locally, CodSpeed will fall back to tinybench since the instrumentation is only available in the CI environment for now. You can now run those benchmarks in your CI to get consistent performance measurements.

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Integrating into a bigger project, multiple benchmark files

Often time you will not be writing your benchmarks in a single file. Indeed, it can become quite difficult to maintain a single file with all your benchmarks as your project grows.
You can find the source code for the following example in the examples of the codspeed-node repository. There are multiple examples available, for CJS, ESM, JavaScript, and TypeScript.
For these kind of situations, we recommend the following approach. Let’s say you have a file structure like this, in a project with TypeScript:
file-structure
.
├── bench
│   ├── fibo.bench.ts
│   ├── foobarbaz.bench.ts
│   └── index.bench.ts
├── package.json
├── src
│   ├── fibonacci.ts
│   └── foobarbaz.ts
└── tsconfig.json
  • The src directory contains the source code of the project. Here we have two files, fibonacci.ts and foobarbaz.ts.
  • The bench directory contains the benchmarks for the project. There is a file for each source file that defines benchmarks for it.
  • The bench/index.bench.ts file is the entry point for the benchmarks. It imports all the other benchmark files and runs them.
bench/fibo.bench.ts
import { Bench } from "tinybench";
import { iterativeFibonacci } from "../../src/fibonacci";

export function registerFiboBenchmarks(bench: Bench) {
  bench
    .add("test_iterative_fibo_10", () => {
      iterativeFibonacci(10);
    })
    .add("test_iterative_fibo_100", () => {
      iterativeFibonacci(100);
    });
}
Here we define a function that takes an instance of Bench as a parameter and then adds some benchmarks to it. This will allow us to add benchmarks to the same suite from multiple files.
bench/index.bench.ts
import { withCodSpeed } from "@codspeed/tinybench-plugin";
import { Bench } from "tinybench";
import { registerFiboBenchmarks } from "./fibo.bench";
import { registerFoobarbazBenchmarks } from "./foobarbaz.bench";

export const bench = withCodSpeed(new Bench());

(async () => {
  registerFiboBenchmarks(bench);
  registerFoobarbazBenchmarks(bench);

  await bench.run();
  console.table(bench.table());
})();
Here all the functions registering benchmarks are executed to import all the benchmarks from the different files. To run the benchmarks, use the following command: 
node -r esbuild-register bench/index.bench.ts
Check out the full for this example: with-typescript-cjs in the codspeed-node repository.

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Running the benchmarks in your CI

To generate performance reports, you need to run the benchmarks in your CI. This allows CodSpeed to automatically run benchmarks and warn you about regressions during development.
If you want more details on how to configure the CodSpeed action, you can check out the Continuous Reporting section.
Here is an example of a GitHub Actions workflow that runs the benchmarks and reports the results to CodSpeed on every push to the main branch and every pull request:
.github/workflows/codspeed.yml
name: CodSpeed Benchmarks

on:
  push:
    branches:
      - "main" # or "master"
  pull_request:
  # `workflow_dispatch` allows CodSpeed to trigger backtest
  # performance analysis in order to generate initial data.
  workflow_dispatch:

jobs:
  benchmarks:
    name: Run benchmarks
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v3
      - name: Install dependencies
        run: npm install
      - name: Run the benchmarks
        uses: CodSpeedHQ/action@v4
        with:
          mode: instrumentation
          run: node benches/bench.mjs
          token: ${{ secrets.CODSPEED_TOKEN }} # optional for public repos

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Limitations

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Async code execution profiling

Execution profiles for async code can sometimes be unreliable, as profiling tools may lose stack trace information due to the event loop.
If your code is fully sync, consider using tinybench’s runSync as an entrypoint to improve accuracy.