Locust – Python Load Testing

Module 1: Introduction to Locust

• Python-native load testing philosophy
• Locust vs JMeter vs k6: Python team choice
• pip install locust and requirements setup
• First locustfile.py: User class and tasks
• Web UI and headless execution modes
• Execution model: users, spawn rate, run time

Module 2: Task Design & User Behaviour

• @task decorator and task weights
• TaskSet for logical task groupings
• Nested TaskSets for complex user journeys
• on_start and on_stop lifecycle methods
• wait_time: between, constant, constant_pacing
• Realistic user behaviour modelling patterns

Module 3: HTTP Testing with Locust

• HttpUser and self.client HTTP methods
• catch_response for custom success/failure logic
• Request naming for reporting grouping
• Session and cookie management
• JSON and form data in request bodies
• Response validation patterns

Module 4: Authentication & Sessions

• Cookie-based authentication
• Token-based auth: JWT retrieval and reuse
• OAuth 2.0 client credentials flow
• Session pooling for efficiency
• Token expiry and automatic refresh
• API key authentication patterns

Module 5: Custom Clients & Protocols

• Custom User classes for non-HTTP protocols
• gRPC load testing with Locust
• WebSocket testing with websocket-client
• Database load testing with SQLAlchemy
• Message queue testing: RabbitMQ and Kafka
• Reusable custom Locust client base classes

Module 6: Test Data & Parameterisation

• CSV data reading for parameterised tests
• JSON fixture files for data injection
• Thread-safe shared data pools across users
• Faker library for dynamic data generation
• Database-driven test data sources
• Environment-based configuration management

Module 7: Distributed Execution & Scaling

• Locust master-worker process architecture
• Docker Compose for local distributed setup
• Kubernetes Locust deployment
• locust-k8s-operator for cloud-native scaling
• User-per-worker capacity planning
• Worker failure handling in distributed runs

Module 8: Reporting, CI/CD & Advanced

• CSV output: response time and request stats
• HTML report generation
• Custom event hooks and plugins
• GitHub Actions performance gates
• Grafana and InfluxDB real-time metrics
• locust-plugins for extended functionality

Our Locust instructors are Python engineers who have built Locust frameworks for API-heavy platforms and microservices backends. They teach Locust as a Python project — leveraging Faker, SQLAlchemy, and asyncio — and have deployed distributed Locust in Docker and Kubernetes for cloud-scale performance validation.