codspeed-setup-harness

Setup Harness

You are a performance engineer helping set up benchmarks and CodSpeed integration for a project. Your goal is to create useful, representative benchmarks and wire them up so CodSpeed can measure and track performance.

Step 1: Analyze the project

Before writing any benchmark code, understand what you're working with:

1. Detect the language and build system: Look at the project structure, package files (Cargo.toml, package.json, pyproject.toml, go.mod, CMakeLists.txt), and source files.

2. Identify existing benchmarks: Check for benchmark files, codspeed.yml, CI workflows mentioning CodSpeed or benchmarks.

3. Identify hot paths: Look at the codebase to understand what the performance-critical code is. Public API functions, data processing pipelines, I/O-heavy operations, and algorithmic code are good candidates.

4. Check CodSpeed auth: Ensure codspeed auth login has been run.

Step 2: Choose the right approach

Based on the language and what the user wants to benchmark, pick the right harness:

Language-specific harnesses (recommended when available)

These integrate deeply with CodSpeed and provide per-benchmark flamegraphs, fine-grained comparison, and simulation mode support.

Language	Framework	How to set up
Rust	divan (recommended), criterion, bencher	Add codspeed-<framework>-compat as dependency using cargo add --rename
Python	pytest-benchmark	Install pytest-codspeed, use @pytest.benchmark or benchmark fixture
Node.js	vitest (recommended), tinybench v5, benchmark.js	Install @codspeed/<framework>-plugin, configure in vitest/test config
Go	go test -bench	No packages needed — CodSpeed instruments go test -bench directly
C/C++	Google Benchmark	Build with CMake, CodSpeed instruments via valgrind-codspeed

Exec harness (universal)

For any language or when you want to benchmark a whole program (not individual functions):

• Use codspeed exec -m <mode> -- <command> for one-off benchmarks
• Or create a codspeed.yml with benchmark definitions for repeatable setups

The exec harness requires no code changes — it instruments the binary externally. This is ideal for:

• Languages without a dedicated CodSpeed integration
• End-to-end benchmarks (full program execution)
• Quick setup when you just want to track a command's performance

Choosing simulation vs walltime mode

• Simulation (default for Rust, Python, Node.js, C/C++): Deterministic CPU simulation, <1% variance, automatic flamegraphs. Best for CPU-bound code. Does not measure system calls or I/O.
• Walltime (default for Go): Measures real execution time including I/O, threading, system calls. Best for I/O-heavy or multi-threaded code. Requires consistent hardware (use CodSpeed Macro Runners in CI).
• Memory: Tracks heap allocations. Best for reducing memory usage. Supported for Rust, C/C++ with libc/jemalloc/mimalloc.

Step 3: Set up the harness

Rust with divan (recommended)

1. Add the dependency:

cargo add divan
cargo add codspeed-divan-compat --rename divan --dev

2. Create a benchmark file in benches/:

// benches/my_bench.rs
use divan;

fn main() {
    divan::main();
}

#[divan::bench]
fn bench_my_function() {
    // Call the function you want to benchmark
    // Use divan::black_box() to prevent compiler optimization
    divan::black_box(my_crate::my_function());
}

3. Add to Cargo.toml:

[[bench]]
name = "my_bench"
harness = false

4. Build and run:

cargo codspeed build -m simulation --bench my_bench
codspeed run -m simulation -- cargo codspeed run --bench my_bench

Rust with criterion

1. Add dependencies:

cargo add criterion --dev
cargo add codspeed-criterion-compat --rename criterion --dev

2. Create benchmark in benches/:

use criterion::{criterion_group, criterion_main, Criterion};

fn bench_my_function(c: &mut Criterion) {
    c.bench_function("my_function", |b| {
        b.iter(|| my_crate::my_function())
    });
}

criterion_group!(benches, bench_my_function);
criterion_main!(benches);

3. Add to Cargo.toml and build/run same as divan.

Python with pytest-codspeed

1. Install:

pip install pytest-codspeed
# or
uv add --dev pytest-codspeed

2. Create benchmark tests:

# tests/test_benchmarks.py
import pytest

def test_my_function(benchmark):
    result = benchmark(my_module.my_function, arg1, arg2)
    # You can still assert on the result
    assert result is not None

# Or using the pedantic API for setup/teardown:
def test_with_setup(benchmark):
    data = prepare_data()
    benchmark.pedantic(my_module.process, args=(data,), rounds=100)

3. Run:

codspeed run -m simulation -- pytest --codspeed

Node.js with vitest (recommended)

1. Install:

npm install -D @codspeed/vitest-plugin
# or
pnpm add -D @codspeed/vitest-plugin

2. Configure vitest (vitest.config.ts):

import { defineConfig } from "vitest/config";
import codspeed from "@codspeed/vitest-plugin";

export default defineConfig({
  plugins: [codspeed()],
});

3. Create benchmark file:

// bench/my.bench.ts
import { bench, describe } from "vitest";

describe("my module", () => {
  bench("my function", () => {
    myFunction();
  });
});

4. Run:

codspeed run -m simulation -- npx vitest bench

Go

No packages needed — CodSpeed instruments go test -bench directly.

1. Create benchmark tests:

// my_test.go
func BenchmarkMyFunction(b *testing.B) {
    for i := 0; i < b.N; i++ {
        MyFunction()
    }
}

2. Run (walltime is the default for Go):

codspeed run -m walltime -- go test -bench . ./...

C/C++ with Google Benchmark

1. Install Google Benchmark (via CMake FetchContent or system package)

2. Create benchmark:

#include <benchmark/benchmark.h>

static void BM_MyFunction(benchmark::State& state) {
    for (auto _ : state) {
        MyFunction();
    }
}
BENCHMARK(BM_MyFunction);

BENCHMARK_MAIN();

3. Build and run with CodSpeed:

cmake -B build && cmake --build build
codspeed run -m simulation -- ./build/my_benchmark

Exec harness (any language)

For benchmarking whole programs without code changes:

1. Create codspeed.yml:

$schema: https://raw.githubusercontent.com/CodSpeedHQ/codspeed/refs/heads/main/schemas/codspeed.schema.json

options:
  warmup-time: "1s"
  max-time: 5s

benchmarks:
  - name: "My program - small input"
    exec: ./my_binary --input small.txt

  - name: "My program - large input"
    exec: ./my_binary --input large.txt
    options:
      max-time: 30s

2. Run:

codspeed run -m walltime

Or for a one-off:

codspeed exec -m walltime -- ./my_binary --input data.txt

Step 4: Write good benchmarks

Good benchmarks are representative, isolated, and stable. Here are guidelines:

• Benchmark real workloads: Use realistic input data and sizes. A sort benchmark on 10 elements tells you nothing about how 10 million elements will perform.

• Avoid benchmarking setup: Use the framework's setup/teardown mechanisms to exclude initialization from measurements.

• Prevent dead code elimination: Use black_box() (Rust), benchmark::DoNotOptimize (C++), or Blackhole.consume (JMH) so the compiler doesn't optimize away unused results.

• Cover the critical path: Benchmark the functions that matter most to your users — the ones called frequently or on the hot path.

• Test multiple scenarios: Different input sizes, different data distributions, edge cases. Performance characteristics often change with scale.

• Keep benchmarks fast: Individual benchmarks should complete in milliseconds to low seconds. CodSpeed handles warmup and repetition — you provide the single iteration.

Step 5: Verify and run

After setting up:

1. Run the benchmarks locally to verify they work:

# For language-specific harnesses
cargo codspeed build -m simulation && codspeed run -m simulation -- cargo codspeed run
# or
codspeed run -m simulation -- pytest --codspeed
# or
codspeed run -m simulation -- npx vitest bench
# etc.

# For exec harness
codspeed run -m walltime

2. Check the output: You should see a results table and a link to the CodSpeed report.

3. Verify flamegraphs: For simulation mode, check that flamegraphs are generated by visiting the report link or using the query_flamegraph MCP tool.

4. Tell the user what was set up, show the first results, and suggest next steps (e.g., adding CI integration, running the optimize skill).