performance-testing

Designing load and stress tests for APIs involves selecting a tool like k6 or JMeter, defining execution parameters for your traffic profile, and setting latency percentile and error rate thresholds to validate throughput and reliability.

Soak testing evaluates system behavior under sustained load over time, spike testing checks reactions to sudden traffic bursts, and stress testing pushes the system beyond capacity limits to identify breaking points and resource exhaustion.

Integrating performance testing into CI/CD requires defining SLO-like thresholds for latency and error rates within your test scripts, allowing automated pipelines to detect regressions and block deployments based on your acceptance signals.

Choosing a performance testing tool depends on your stack: use k6 or Gatling for API and microservice testing, JMeter for enterprise load generation, Locust for Python-based scenarios, and JMH or Go benchmarks for micro-level code profiling.

Profiling root causes of latency issues requires attaching diagnostic tools during test execution, such as JFR or async-profiler for JVM applications, pprof for Go, and py-spy for Python to capture heap dumps and identify bottlenecks.

JMeter works for microservice stress and soak testing by simulating heavy traffic loads over extended periods, though modern alternatives like k6 or Gatling might offer better programmability and CI/CD automation for complex scenarios.