How to set up distributed load testing with Locust?

Run a master node with 'locust -f yourfile.py --master' and worker nodes with 'locust -f yourfile.py --worker --master-host= '. The README mentions it supports distributed testing across multiple machines for scalability.

Locust vs JMeter for performance testing?

Locust is better for teams comfortable with Python and needing flexible, code-based test scenarios, while JMeter offers a GUI and more built-in features out of the box but uses XML configurations. Locust excels in scalability and real-time UI adjustments.

How to write a custom client for testing non-HTTP protocols in Locust?

Extend the base User class and implement client methods using Python libraries for the protocol, as documented in the 'testing other systems' section. Community extensions on GitHub can serve as examples.

Can Locust integrate with CI/CD pipelines?

Yes, Locust can run in headless mode without the UI using command-line options, making it suitable for automation. Export results for analysis, but you may need custom scripts for detailed reporting integration.

What are common performance issues with Locust under high load?

Due to its event-based gevent architecture, high concurrency can lead to increased memory usage and potential bottlenecks if not properly tuned. Monitoring system resources and optimizing Python code is recommended.

How to visualize Locust test results in Grafana?

Use community plugins like locust-plugins to send real-time data to TimescaleDB, then connect Grafana for dashboards. The README links to examples, but this requires additional setup and dependencies.

Open-Awesome

locust

MITPython2.46.1

An open-source load testing tool that lets you write scalable performance tests in plain Python.

Visit Website GitHub

28.0k stars3.2k forks0 contributors

What is locust?

Locust is an open-source load testing tool that allows developers to write performance tests in plain Python code. It simulates user behavior to test the scalability and responsiveness of web services, APIs, and other systems under high concurrency. Unlike GUI-based tools, it provides a code-first approach that integrates with standard development workflows.

Target Audience

Developers, QA engineers, and DevOps professionals who need to perform scalable load and performance testing on web applications, APIs, or custom protocols. It's ideal for teams that prefer scripting tests in Python over using proprietary tools.

Value Proposition

Locust stands out by offering a developer-friendly, Python-based testing environment that avoids the limitations of domain-specific languages or graphical interfaces. Its distributed architecture and real-time web UI enable testing at scale while maintaining flexibility through a pluggable, hackable design.

Overview

Write scalable load tests in plain Python 🚗💨

Use Cases

Best For

Testing web APIs under high concurrent user loads
Performance validation in CI/CD pipelines
Simulating complex user scenarios with conditional logic
Distributed load testing across multiple machines
Custom protocol testing beyond HTTP
Real-time monitoring and adjustment of load tests

Not Ideal For

Teams that prefer GUI-based drag-and-drop test creation without writing code
Environments where Python is not used or supported in the tech stack
Quick, one-off smoke tests for simple endpoints where CLI tools like curl or Postman are sufficient
Testing protocols with no community extensions, requiring extensive custom client development

Pros & Cons

Pros

Plain Python Scenarios

Tests are written in regular Python code, allowing use of loops, conditionals, and standard libraries without callbacks or XML, as shown in the example code snippet for defining user behavior.

Distributed Scalability

Uses event-based gevent to handle hundreds of thousands of concurrent users and supports running tests over multiple machines, enabling testing of highly concurrent systems as highlighted in the README.

Real-time Web UI

Includes an interactive web interface for live monitoring and dynamic load adjustment during tests, with screenshots in the README showing real-time charts and statistics.

Protocol Flexibility

Can test any system by writing custom clients or using community extensions, making it adaptable beyond HTTP, though this requires additional effort as noted in the documentation links.

Cons

Limited Built-in Features

The codebase is intentionally small and hackable, meaning users must write custom code for advanced reporting, load shapes, or protocol support, increasing setup complexity compared to more comprehensive tools.

Python Dependency Barrier

Requires Python knowledge and environment setup, which can be a hurdle for teams unfamiliar with Python or in polyglot ecosystems, limiting accessibility for non-developers.

Custom Protocol Effort

Testing non-HTTP protocols necessitates writing custom clients from scratch, which is time-consuming and error-prone compared to tools with native protocol support out of the box.

Frequently Asked Questions

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