load-testing

API load testing identifies performance bottlenecks by designing and executing tests like baseline, stress, spike, and soak scenarios. It verifies system throughput against measurable targets such as SLOs and latency percentiles across distributed backend services.

Integrating load testing with CI catches performance regressions early by automating test execution. You can design test scenarios using tools like k6 or Gatling to validate throughput and latency SLOs automatically during the deployment pipeline.

Yes, you can run stress, spike, and soak tests for microservices using tools like k6, JMeter, Gatling, Locust, or Artillery. These tools help validate system behavior and throughput under sustained or extreme load conditions.

Define performance goals by establishing concrete metrics for throughput and latency percentiles. Setting measurable SLO targets before executing load tests ensures you can accurately validate API baseline performance and identify bottlenecks under expected traffic.

Yes, this load testing approach is applicable to distributed systems and microservices. It designs test scenarios to evaluate backend API services, ensuring valid data management and controlled test environments across complex architectures.