> For the complete documentation index, see [llms.txt](https://www.pranaypourkar.co.in/the-programmers-guide/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.pranaypourkar.co.in/the-programmers-guide/software-testing/testing-fundamentals/software-testing-methodologies/non-functional-testing/performance-testing/load-testing/process.md). # Process ### **About** This describes the full lifecycle of planning, designing, executing, and analyzing a load test. It is written to support teams who want to perform meaningful, repeatable, and scalable load testing as part of their QA or DevOps practices. ### **1. Define the Objective** Start by answering **why** you are performing the load test. Without a clear objective, the test may not provide actionable insights. Common objectives include: * Validate system behavior under expected traffic * Confirm response time meets service-level targets * Identify scalability limits or bottlenecks * Evaluate performance after infrastructure or code changes * Validate that recent changes do not degrade throughput Document your objective. Make it specific and measurable. ### **2. Understand the System Under Test (SUT)** Gain a complete understanding of what you are testing. This includes: * APIs, services, databases, and frontends involved * Third-party dependencies (e.g., payment gateways, auth services) * Environment details (e.g., staging or pre-prod configuration) * Any limits, such as API rate limiting or queue size Establish the scope: are you testing a single component or end-to-end flow? ### **3. Collect Usage and Load Requirements** Use production logs, analytics, or stakeholder input to determine: * Expected number of concurrent users or sessions * Peak vs average load * Session duration and think times * Request distribution across endpoints (e.g., 80% GET, 20% POST) * Historical spikes or seasonal traffic patterns Without this data, your load test risks being unrealistic or irrelevant. ### **4. Design Test Scenarios** Define how virtual users will behave during the test. For example: * 70% of users log in and fetch dashboard * 20% download reports * 10% trigger background jobs Include: * Authentication * Navigation flows * Data dependencies (e.g., valid user IDs or tokens) * Wait times between actions Design scenarios to simulate **real-world usage**, not just raw request volume. ### **5. Choose the Right Tool** Select a load testing tool based on: * Target system type (API, UI, database) * Team skills (e.g., scripting in JavaScript, Python, DSL) * Support for distributed execution * Reporting and integrations Examples: * **JMeter** for GUI-driven configuration and complex scripting * **k6** for developer-centric JavaScript scripting * **Gatling** for concise DSL-based tests * **Locust** for Python scripting flexibility ### **6. Prepare the Test Environment** The environment should closely match production: * Same hardware configuration * Same database size or indexing * Same network latency, authentication, and middleware Ensure: * Monitoring and logging are enabled * Autoscaling (if any) is configured as it would be in production * No other teams are testing or deploying during your test window ### **7. Generate or Load Test Data** Many scenarios require: * Auth tokens or API keys * Valid user accounts or customer records * Pre-existing data (e.g., product catalog, transaction history) Options: * Static CSV data * Dynamic data generation * REST API calls in setup phase * Test-specific database snapshots Avoid using production data directly to prevent risk of leakage. ### **8. Script and Configure the Test** Build test scripts that include: * Defined user flows * Parameterization of inputs (e.g., user IDs) * Assertions (e.g., status code = 200) * Think times and pacing * Configurable test duration, ramp-up time, and concurrency Validate scripts locally using small thread counts before scaling. ### **9. Execute the Test** Start with a **dry run** or **baseline run**: * Check environment readiness * Validate monitoring and log collection * Confirm no errors in script logic Then run the actual load test: * Start with ramp-up period * Maintain load for steady-state duration * Collect results at regular intervals Avoid making configuration changes during the test run. ### **10. Monitor System Behavior During Execution** Monitor key components: * Application servers: CPU, memory, thread pool usage * Databases: connections, query times, locks * Queues: backlog size, consumer lag * API gateways: rate limits, throttling * Logs: exceptions, timeouts, garbage collection activity Use dashboards (e.g., Grafana, CloudWatch, Prometheus) to observe in real-time. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. 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