k6 Performance Testing

You measure API latency and error rates by executing k6 load tests against HTTP endpoints, applying thresholds like p(95) latency and failure rate limits. This validates request performance while scaling virtual users to simulate real traffic.

Smoke testing provides quick validation with minimal traffic, load testing evaluates performance under expected traffic levels, and stress testing identifies breaking points by pushing virtual users beyond normal operating capacity.

You export k6 test results to JSON by using the summary-export feature, generating a file like k6_summary.json. Downstream reporting tools can then extract key metrics including p(95) latency, failed request rates, and request counts.

Yes, k6 performance testing is designed for staging and test environments to validate API stability. It measures latency percentiles and request failure rates under varying traffic scenarios to identify bottlenecks before release.

Yes, you need to write a k6 JavaScript test script that defines options and thresholds for your HTTP endpoints. This scripting approach ensures repeatable test runs that validate specific latency and error rate criteria.

You can extract metrics like p(95) latency, failed request rate, virtual user counts, and total request counts from a k6 JSON summary export. These metrics help evaluate API behavior under smoke, load, and stress scenarios.