Load Testing Your Application with Locust:

As developers and DevOps engineers, we often focus on functionality and deployment—but performance under pressure is just as critical. Whether you're launching a new product, scaling up your backend, or optimizing an API, load testing can help prevent slowdowns and crashes.

In this post, we'll explore Locust, a powerful and flexible Python-based tool that lets you simulate thousands of concurrent users to test your system's behaviour under load.

What is Locust?

Locust is an open-source load testing tool written in Python. It allows you to define user behavior in code and then simulate many users to hit your system simultaneously.

Why use Locust?

• Python-based: Tests are written in Python—easy to read, flexible to customize.

• Web UI: Launch and control your tests from a real-time web dashboard.

• CLI & Distributed: Run locally or across multiple machines for large-scale tests.

• Cloud Option: Use Locust Cloud if you don’t want to write code. Built by the creators of open-source Locust, it lets you run and manage load tests via an intuitive web interface without coding in Python.

Installing Locust

To get started, make sure you have Python 3 installed. Then:

pip install locust

You can verify installation with:

locust --version

Writing Your First Locust Test

Create a file called url_test.py:

from locust import HttpUser, task

class MyUser(HttpUser):
    @task
    def index(self):
        self.client.get("/")
        self.client.get("/home")
        self.client.get("/about")

Here’s what’s happening:

• HttpUser: Simulates a user making HTTP requests

• @task: Defines what the user does (GET homepage, GET /about)

Running the Load Test

Use this command:

python -m locust -f url_test.py

Then open your browser to:

http://localhost:8089

You’ll see a UI to set:

• Number of users to simulate

• Spawn rate (users per second)

Click Start Swarming and watch the dashboard populate in real-time.

Understanding the Results

The UI shows you:

• Requests per second (RPS)

• Failures

• Response times (avg, min, max)

• Charts for load and latency

Use this to identify:

• Slow endpoints

• Bottlenecks under load

• System capacity before performance degrades