Performance Engineering

Performance Testing: Tools and Techniques Performance testing measures how a system behaves under load. It helps you confirm that response times stay acceptable, errors stay rare, and the system can scale with more users or data. Clear goals, good planning, and repeatable tests make results trustworthy for developers and stakeholders. Techniques Load testing: simulate typical user activity to verify targets for response time and throughput. Stress testing: push beyond normal limits to find breaking points and observe recovery. Endurance (soak) testing: run long enough to reveal leaks or degradation over time. Spike testing: abruptly increase load to test how the system handles sudden pressure. Tools JMeter: a mature, flexible tool with a wide range of test plans and protocols. k6: script-driven tests in JavaScript, fast and easy to automate in CI. Gatling: strong for HTTP APIs, with a concise DSL and good reporting. Locust: Python-based and scalable with distributed workers. Artillery: Node.js friendly for API and microservice tests. wrk / vegeta: lightweight, command-line options for quick checks. How to build a test plan Define objectives: target latency, error rate, and expected throughput. Identify critical paths: login, search, checkout, or API calls. Create realistic data: user profiles, product catalogs, and session behavior. Pick an environment: production-like with controlled data; avoid live customer data when possible. Choose metrics: p95, p99 latency; average latency; error rate; requests per second. Design scenarios: baseline load, peak load, and soak scenarios. Run and iterate: start small, compare with previous results, and refine. Interpreting results Look beyond average times. Check percentile metrics, error distribution, and resource usage on servers and databases. A small, steady increase in latency under load can hide a bottleneck in the database or a slow external service. Use monitoring dashboards to connect front-end response times with back-end events. ...