Most load-testing tools make you describe traffic in a config format or a clunky GUI. Locust takes a more natural approach: you define what a user does as plain Python code, then Locust spawns thousands of these simulated users against your app and reports how it holds up. Because scenarios are just code, realistic user behavior - log in, browse, filter, submit - is easy to express, version, and reuse.
User Behavior as Code
A load test is a Python class describing what a user does and how often:
from locust import HttpUser, task
class Visitor(HttpUser):
@task
def browse_tools(self):
self.client.get('/tools')
self.client.get('/jsonapi/node/dev_tool')
Swarm and Watch
You start Locust, tell it how many users to simulate and how fast to ramp up, and watch a live web dashboard of request rates, response times, and failures. It scales to large loads by distributing across multiple machines, so you can push a realistic amount of traffic and find where things start to bend.
Rough Edges and Limitations
- You model the load. The results are only as realistic as the scenarios you write.
- Test where it's safe. Load testing is for staging or carefully-controlled runs, not blindly against production.
- Client-side limits. Very high loads need distributed workers and enough machine to generate them.
For understanding how a web app behaves under real concurrency before your users find out, Locust makes writing and running that test refreshingly straightforward.
Internal link suggestion: link "how it holds up" to the Caddy or Glances post for the serving and monitoring side of performance.