Learn Locust load and performance testing with Python for web apps and REST APIs, stress testing and real-world projects
β±οΈ Length: 6.8 total hours
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- Course Overview
- Engage with a comprehensive curriculum designed to transition you from a testing novice to a proficient performance engineer using the Locust framework.
- Discover the internal mechanics of Locust’s event-driven architecture and how it utilizes the gevent library to simulate thousands of concurrent users with minimal hardware overhead.
- Learn the fundamental differences between thread-based testing tools and the modern, asynchronous approach provided by Python-based scripting.
- Understand the complete performance testing lifecycle, starting from requirement gathering and environment setup to script development and final report generation.
- Explore how to build modular and maintainable test suites that can evolve alongside your applicationβs codebase.
- Analyze the Locust Web UI in depth to monitor real-time statistics, response time percentiles, and failure rates during active test execution.
- Deep dive into distributed load testing, learning how to orchestrate a master-worker setup to generate massive traffic from multiple machines simultaneously.
- Study the nuances of Stress Testing and Spike Testing to identify the exact breaking point of your infrastructure and software components.
- Focus on Developer-Centric Testing, where performance scripts are treated as code, versioned in Git, and integrated into the broader development ecosystem.
- Requirements / Prerequisites
- Possess a foundational understanding of Python, specifically regarding classes, decorators, and basic data structures like dictionaries and lists.
- Have a working knowledge of HTTP protocols, including status codes, headers, cookies, and the differences between various request methods.
- Ensure you have administrative access to your local machine to install Python, the pip package manager, and necessary virtual environments.
- A basic familiarity with command-line interfaces (CLI) is recommended for executing scripts and managing distributed worker nodes.
- No prior experience with JMeter or LoadRunner is required, as this course builds performance testing concepts from the ground up using a code-first mentality.
- Access to a modern code editor such as Visual Studio Code or PyCharm to facilitate efficient script writing and debugging processes.
- Skills Covered / Tools Used
- Master the HttpUser class to define core user behaviors and manage session persistence throughout the duration of a load test.
- Implement TaskSets and SequentialTaskSets to create complex, multi-step user journeys and logical workflows within your scripts.
- Utilize Task Weighting to simulate realistic user distributions across different features and endpoints of your web application.
- Incorporate Wait Time functions such as constant, between, and constant_pacing to model human-like interaction pauses and request frequencies.
- Develop custom Python functions to handle dynamic data injection, ensuring each virtual user interacts with unique credentials or parameters.
- Learn to use JSON and BeautifulSoup libraries within your Locust scripts to parse API responses and extract data for subsequent requests.
- Apply Event Hooks to extend Locust’s functionality, allowing for custom logging, data setup routines, and teardown procedures.
- Configure Custom Clients for non-HTTP protocols, enabling you to load test databases, message queues, or custom socket connections.
- Execute Headless Load Tests for automation purposes, generating CSV reports and console-based summaries without the need for a graphical interface.
- Manage Load Test Configurations through configuration files and environment variables to ensure portability across different staging environments.
- Benefits / Outcomes
- Acquire the ability to validate the scalability and reliability of modern RESTful APIs and microservices under varying degrees of load.
- Become proficient in identifying performance bottlenecks, such as slow database queries, memory leaks, or inefficient networking configurations.
- Gain the confidence to provide data-driven recommendations to stakeholders regarding infrastructure capacity and deployment readiness.
- Learn to integrate performance tests into CI/CD pipelines (e.g., GitHub Actions, Jenkins), ensuring that performance regressions are caught early in the SDLC.
- Build a robust professional portfolio featuring real-world projects that showcase your ability to handle high-concurrency testing scenarios.
- Develop a performance-first mindset, allowing you to contribute effectively to DevOps and Site Reliability Engineering (SRE) discussions.
- Transition from manual testing roles into technical automation and performance engineering positions with a high-demand skill set.
- Master the art of interpreting performance metrics like Latency, Throughput (RPS), and Error Rates to provide meaningful insights into system health.
- PROS
- High Efficiency: Locust’s light footprint allows for massive scale on modest hardware compared to Java-based alternatives.
- Pure Python: Leverages the vast ecosystem of Python libraries, making it incredibly flexible for complex testing logic.
- Real-time Insights: The interactive web dashboard provides immediate feedback on system behavior during the test.
- Scalability: Easily scales from a single local script to a massive distributed network of workers in the cloud.
- Code-as-Test: Promotes version control and collaborative script development, aligning with modern software engineering practices.
- CONS
- Coding Dependency: Unlike GUI-based tools, this course requires a commitment to writing code, which may be challenging for those with zero programming background.
Learning Tracks: English,IT & Software,Other IT & Software
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