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Start with a service goal, not a load-test default
Choose an outcome that matters to users: for example, keeping important requests successful and response times within the service’s objectives. Set the pass/fail criteria before the first run, using your own requirements and observed baseline rather than copying a vendor’s example as a universal standard.
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For an API, a goal might cover the proportion of failed requests and a response-time percentile on a critical endpoint. For a user journey, include whether the journey actually completed correctly. A fast response containing the wrong data is not a healthy result.
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Write scenarios around important API paths or user journeys. Decide what question each test answers: a short smoke check can catch basic problems, while staged or higher-load scenarios show how the service behaves as demand rises. A test is only useful to the extent that its workload and environment represent the use case you want to protect.
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- Document the endpoints or user actions, request mix, test data, and load pattern.
- Record relevant differences from production, such as available capacity or dependencies.
- Use an isolated or pre-release environment when a test could affect users or shared systems. Do not direct uncontrolled high traffic at production.
Measure distinct signals and define pass/fail thresholds
Latency, errors, throughput, and correctness answer different questions. In k6, http_req_duration measures request duration and is commonly examined using percentiles; http_req_failed reports the failed-request rate; http_reqs describes generated request volume or rate; and checks let the script assert response properties. Select the signals and limits that match your service objectives rather than treating an average response time as a complete verdict.
This k6 threshold pattern follows Grafana’s API guide, but its values are illustrative—not industry-wide standards. Replace them with limits justified by your service requirements and baseline. A failed threshold makes the k6 run fail and returns a non-zero CLI exit code, which a CI job can use as a deployment gate.
export const options = {
thresholds: {
http_req_failed: ['rate<0.01'],
http_req_duration: ['p(95)<200'],
},
};Here the example asks for a failed-request rate below 1% and a 95th-percentile request duration below 200 ms. Those numbers are examples from Grafana’s guide, not recommendations for every service. See Grafana’s k6 API load-testing guide for the context and threshold behavior.
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Place tests where the feedback is useful
Choose a pipeline stage by balancing feedback time against the confidence a scenario provides. Grafana Labs says load tests often take 3 to 15 minutes or more; that is guidance from its documentation, not a runtime promise for every test. If a broad scenario would delay every change, use a smaller bounded check for frequent feedback and run deeper tests on a schedule or before release. Keep a suitable pre-release environment available for those broader runs.
For GitHub Actions, Grafana Labs documents official k6 actions for running tests from a workflow. Consult the current k6 GitHub Actions documentation for action usage. Review and pin dependencies in line with your team’s normal supply-chain and maintenance practices before adopting a workflow.
Build the CI job around a bounded, reproducible run
The same pattern applies across CI providers: install or invoke the selected load-testing tool, supply the target and safe configuration, run a bounded scenario, retain useful output, and let the tool’s exit status determine the job result. Keep scripts and thresholds under version control with the application where practical.
- Select the target deliberately. Point the job at the intended test environment and provide credentials through the CI provider’s secret mechanism, not in the script or repository.
- Set explicit limits. Bound the scenario’s duration and load to what the environment can safely handle. Coordinate tests that might affect users or shared services.
- Run the scenario. Use the tool’s supported CI integration or CLI invocation. Make sure the job does not mask a non-zero exit status from a failed threshold.
- Preserve diagnostic output. Retain the summary and relevant test output or time-series data so a failure can be examined after the job ends.
- Compare and route. Where your workflow supports it, compare results with a baseline and send failure details to the team responsible for investigation.
A tool-specific workflow should follow that tool’s current documentation; the k6 threshold example above is portable as a script pattern, but it is not a complete GitHub Actions workflow. Avoid copying an unreviewed workflow or exposing test credentials while adapting it.
Make a failed run actionable
When a job fails unexpectedly, first establish whether the change, the test, or the environment explains the result. Inspect the threshold that failed, the scenario output, and the environment before loosening a limit.
- Latency threshold breached: identify which requests or journey segments drove the percentile, then compare with the baseline and recent application or dependency changes.
- Failure-rate threshold breached: check response codes and functional checks, and verify that the target and its dependencies were available.
- Throughput differs from expectation: confirm the intended load pattern and whether the test environment can generate or sustain it.
- Intermittent pipeline result: examine test stability, environment drift, and workload assumptions before changing thresholds.
- Job is too slow: reconsider whether the scenario belongs on every change or in a scheduled or pre-release stage; preserve a quick test where it provides useful feedback.
Interpret results narrowly and iterate
Keep the run summary and enough underlying output to understand what happened, not just a pass/fail badge. Revisit scenarios and thresholds as traffic patterns and service objectives change. A passing run supports only the workload and environment it exercised; it cannot establish performance under every possible load shape.
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Example cURL request (replace the target URL as needed):
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Frequently Asked Questions
Does a passing CI performance test prove the application will meet every production demand?
No. It describes only the workload and environment exercised by that run.
Are the k6 threshold values shown here recommended limits?
No. They are illustrative values from Grafana’s API guide; choose limits based on your service objectives and baseline.
How long should a CI load test run?
There is no universal duration. Grafana Labs says load tests often take 3 to 15 minutes or more, but actual time depends on the scenario and environment.
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