load-testing

Load testing measures system responsiveness, throughput, and stability to prevent outages. You need it to validate capacity planning, identify bottlenecks, and ensure your API meets reliability targets before real users impact production.

Design a load testing plan by defining test objectives, scenarios, and success criteria for load, stress, endurance, and spike tests. Build k6 scripts targeting API response times and error-rate SLAs, then validate performance using a repeatable execution checklist.

Analyze load testing results by examining response time distributions, throughput limits, error rate patterns, and CPU/memory/I/O utilization. Systematic result analysis pinpoints bottlenecks and provides root-cause-oriented optimization recommendations for scalability.

Choose between k6, JMeter, Locust, and Gatling based on scripting preferences and performance testing requirements. Tool selection depends on your test scenarios, capacity planning needs, and specific API testing goals defined during test planning strategy.

Track responsiveness, throughput limits, error rate patterns, and CPU/memory/I/O utilization during stress and spike testing. These metrics validate capacity planning, measure scalability, and highlight performance regressions under extreme load conditions.