# Artillery — Modern Load Testing for HTTP, WebSocket & More

> Node.js load testing toolkit with YAML scenarios covering HTTP, WebSocket, gRPC and Playwright, plus distributed runs on AWS Fargate.

## Install

Save in your project root:

# Artillery — Modern Load Testing for HTTP, WebSocket, and Beyond

## Quick Use
```bash
# Install
npm install -g artillery

# Run a quick smoke test
artillery quick --count 100 --num 5 https://api.example.com/health

# Run a scenario file
cat > load.yml <<'YAML'
config:
  target: "https://api.example.com"
  phases:
    - duration: 60
      arrivalRate: 20
scenarios:
  - flow:
      - get:
          url: "/users/{{ $randomNumber(1, 100) }}"
YAML
artillery run load.yml
```

## Introduction
Artillery is a Node.js-based load testing toolkit for HTTP, WebSocket, Socket.IO, gRPC, Kinesis, and custom protocols. It started as a simple CLI and has grown into a full distributed testing platform — the same YAML scenario runs on a laptop, in GitHub Actions, or scaled out across AWS Fargate with one flag.

## What Artillery Does
- Executes YAML-defined scenarios against HTTP, WebSocket, Socket.IO, gRPC, Playwright, and more.
- Generates arrival-rate–based traffic (open model) so results reflect production load patterns.
- Exports metrics to Datadog, CloudWatch, Honeycomb, InfluxDB, Prometheus, and OpenTelemetry.
- Runs browser tests with Playwright engine for real end-user journey load testing.
- Distributes load across AWS Fargate or Kubernetes with a single `artillery run-fargate` command.

## Architecture Overview
Artillery Core is a Node.js runtime that interprets YAML scenarios into a set of virtual-user pipelines. Each phase produces a Poisson-style arrival schedule at the configured rate, and each virtual user walks through its flow — HTTP requests, variable capture/setting, think-time, assertions — independently. Engines for non-HTTP protocols plug in as npm modules. For scale-out, `artillery run-fargate` packages the scenario into a task image and launches N workers with result aggregation over SQS.

## Self-Hosting & Configuration
- Install via npm or the Docker image `artilleryio/artillery:latest` for CI.
- Keep shared data in CSV files referenced via the `payload` option for parameterized runs.
- Use `hooks` in processor JS files to sign requests, generate tokens, or shape payloads.
- Configure thresholds (`ensure.thresholds`) so CI fails on p95 / error-rate regressions.
- Ship metrics via `plugins.publish-metrics` — works out of the box with OpenTelemetry OTLP.

## Key Features
- Scenario-level DSL with variables, conditionals, loops, and request chaining.
- Plugin ecosystem: metrics, APDEX, expect-style assertions, custom engines.
- Playwright engine turns end-to-end user journeys into performance tests.
- Distributed runs on AWS Fargate without provisioning infrastructure.
- OpenAPI / Swagger importer scaffolds a scenario from a spec in seconds.

## Comparison with Similar Tools
- **k6** — JS-scripted, Go engine; Artillery is YAML-first with a JS extension surface.
- **JMeter** — mature and GUI-driven; Artillery is code-first and CI-native.
- **Gatling** — Scala-based, high performance; steeper learning curve.
- **Locust** — Python-based user classes; Artillery focuses on arrival-rate models.
- **Vegeta** — laser-focused on constant HTTP rate; Artillery covers more protocols and scenarios.

## FAQ
**Q:** Is Artillery free?
A: The CLI and core engine are open-source. Artillery Cloud is an optional SaaS for dashboards.

**Q:** Can I run distributed tests without AWS?
A: Yes — the k8s-based runner is community-supported, or run Artillery processes yourself and aggregate metrics in Prometheus.

**Q:** How does it handle authentication?
A: Use processor hooks to attach tokens, or the built-in `beforeRequest` handlers.

**Q:** Can it replay production traffic?
A: With the `playback` plugin and HAR imports; otherwise scenarios are usually richer than raw replay.

## Sources
- https://github.com/artilleryio/artillery
- https://www.artillery.io/docs


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Source: https://tokrepo.com/en/workflows/729c7c2c-38d7-11f1-9bc6-00163e2b0d79
Author: AI Open Source