What is Locust

Locust is a Python-based testing tool used for load testing and user behavior simulation.  Load testing is the practice of testing software applications. Its main purpose of stressing the application’s capabilities.

Locust is a tool that creates a set of testing functions that simulate a heavy number of users.  This will determine the main breaking point in terms of performance, security, and application load management.

In addition, it presents test results in a dashboard. One of the significant features of Locust is its well-documented code source, which means that its library can easily be understood and implemented by the software engineering team in a matter of hours.


While Locust is entirely based on Python, the Locust library doesn’t come preinstalled in the Python environment. You will need to install Locust via pip:

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Getting Started

In below example, you will run a login registration script.
The tested application can be a Python, Java, or any other programming-based application.
To utilize the Locust library, follow the subsequent steps:

  • Import HttpUser, task from locust.
  • Implement a testing class called TestUser and inherit from HttpUser.
  • Define your class’s login and registration tasks using @tasks annotation.
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Test Configuration

Configure Locust by running the locust command in your CLI.

The configuration file will contain some values that determine how your load testing process will be performed.  These values can be the number of users used in the load testing process or the runtime of the load testing process.
Create a configuration file under the name locust.conf that will contain the configuration settings:

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Run the locust command in your command line with the name of your configuration file as an argument at the end of your command:

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Test Report

You can find below the Locust user-friendly web-based test report after test finished

Benefits of Locust

The main benefits of Locust can be defined as:

  • A user-friendly web-based UI.
  • Brings a set of dashboards, visualizations, and test reports that summarize the load testing process.
  • Enables the testing team to run multiple test scripts to find out the main performance and load handling problems..
  • Enables the testing team to quickly increase the number of test cases and compare the test results for every task, user, and request.