Performance testing in Kubernetes can mean two different things: measuring how an application responds to traffic, or measuring how the cluster handles Kubernetes-scale workloads and control-plane activity. Choose the test approach to match the question: a load generator such as Grafana k6 exercises application endpoints, while ClusterLoader2 is designed for Kubernetes scalability and performance scenarios. For either kind of test, define pass criteria, capture relevant signals, and keep the test configuration consistent enough to compare results.
1. Decide what you need to measure
Write down the question before choosing tools. An application test asks how a service behaves under a specified traffic pattern. A cluster test asks whether Kubernetes can reach and sustain desired states—such as creating or scheduling a workload—at a specified throughput. A combined test examines both service outcomes and the cluster behavior that may affect them.
- Service capacity or latency: Can the application meet its response-time and error-rate requirements at expected or peak demand?
- Workload scaling: How does the service behave as replicas, traffic, or resource demand change?
- Cluster scalability: Can the cluster schedule and manage the desired workload state at the required pace?
2. Set pass criteria before generating load
Specify what counts as success for this workload: for example, an acceptable request-duration threshold, maximum failed-request rate, required capacity, or time to reach a target cluster state. Choose thresholds from the service’s requirements and user impact; there is no universal Kubernetes latency or utilization target that makes every system healthy.
For a cluster scenario, define the desired state and throughput you expect to achieve. ClusterLoader2 uses test definitions to describe target states, throughput, and measurements. Document the criteria so the result is not judged only by whether the test completed.
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3. Choose the right test category and tool
These tools and data sources have different jobs; metrics collection is not a substitute for load generation, and an application traffic test does not by itself establish cluster scalability.
| Tool or category | Best fit | What to evaluate |
|---|---|---|
| Grafana k6 | Generating application or API traffic, including load, spike, stress, and soak patterns. | Traffic model, protocol needs, emitted metrics, where tests run, CI/CD integration, and whether open-source or cloud capabilities fit the test. |
| ClusterLoader2 | Kubernetes cluster scalability and performance scenarios. | Desired cluster states, throughput, measurement definitions, Prometheus observability, and the repository/version state used for the run. |
| Kubernetes Metrics API / metrics-server | Basic pod and node CPU and memory context, including data used by HPA/VPA and available through kubectl top. |
Whether these basic resource signals are sufficient or a fuller monitoring pipeline is needed. This is an observation source, not a load generator. |
| Prometheus-format component metrics | Signals from Kubernetes components and system endpoints. | Which endpoints and metric stability levels apply to the deployed Kubernetes version and the diagnosis. |
4. Model a realistic workload or target state
For application traffic
Specify the arrival pattern or concurrency, request mix, test duration, and ramp-up or ramp-down behavior. Include the endpoints and user journeys that matter to the question. A steady load, a brief spike, a stress test that pushes toward a limit, and a long soak test reveal different behaviors; choose one deliberately rather than treating them as interchangeable.
For a cluster scalability test
Describe the Kubernetes state the test should create or maintain and the throughput at which it should get there. ClusterLoader2 expresses these scenarios through test configuration, including desired states and measurements. Select state sizes and throughput from your own requirements; do not treat a sample scenario as a universal capacity target.
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5. Make the environment representative and record it
Record the Kubernetes version, application and workload configuration, resource requests and limits, dependencies, and relevant cluster topology. Note changes that could alter the outcome, such as autoscaling settings or test placement. Version matters for Kubernetes metrics: names, availability, and stability can differ by release. Use the metrics reference for the Kubernetes version actually deployed, not merely the newest reference.
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Check that application outcomes and the cluster signals needed to interpret them are visible before starting. Kubernetes’ resource metrics pipeline supplies a basic set of CPU and memory metrics through the Metrics API; Kubernetes documents it as a minimum resource-metrics facility, not a complete monitoring system. See Tools for Monitoring Resources for the distinction between resource views and broader monitoring.
Choose additional monitoring according to the question. A request test may need service-level latency and error signals; a scheduling or control-plane investigation may require component metrics. Avoid discovering after the run that the decisive signal was never collected.
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7. Capture a baseline
Record the system at a known operating point before the controlled test, such as without the test-generated load or at a documented normal load. Capture the same application and cluster measurements you plan to use during the run. A baseline gives the later results context; it is not a performance target by itself.
8. Run a controlled test
Keep the workload definition and configuration stable when comparing runs. For application testing, execute the selected traffic pattern against the intended endpoint. For cluster testing, run the configured target-state and throughput scenario. Record the start and end times and any changes or disruptions during execution so they are not mistaken for effects of the tested change.
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9. Track application outcomes
For HTTP tests, begin with request volume, failed requests, and request duration. k6 documents the built-in metrics http_reqs, http_req_failed, and http_req_duration as useful starting points when metric selection is uncertain; the right metrics still depend on the test goal. Compare durations and failures with the criteria you set, and examine distributions such as percentiles rather than relying only on an average.
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These signals establish what happened to requests, not why. Pair them with service-specific and cluster measurements when diagnosing a change in latency or errors.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.10. Inspect pod and node resource behavior
Use the Metrics API or kubectl top for an initial view of pod and node CPU and memory. The resource metrics pipeline can also supply data to HPA and VPA. These readings are useful context, but they are not a complete history or diagnosis of performance: the API provides a limited resource-metric set rather than full observability. Add an appropriate monitoring pipeline when the test needs more detail or longer-term correlation.
11. Correlate Kubernetes component signals
Kubernetes components expose metrics, generally in Prometheus format, and kubelet has distinct metrics endpoints. When the test points toward scheduling, API activity, node behavior, or another component, inspect relevant signals from those sources alongside the application results. Consult Kubernetes system component metrics and check the version-matched metric reference for availability and stability. A metric marked beta or alpha may not be as dependable for a durable dashboard as one marked stable.
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12. Interpret bottlenecks without overclaiming
Look for signals that change together during the same test interval. For example, rising request durations alongside resource pressure may justify investigating resource constraints, while changes in cluster component metrics may point to a separate control-plane or scheduling question. Correlation narrows the investigation but does not prove causation; a CPU or memory summary alone cannot establish why requests slowed down.
- Compare the result with the stated pass criteria, not an assumed universal target.
- Check whether errors or latency coincide with a change in pod/node resource signals or relevant component metrics.
- Separate what the measurements show from what they do not establish; investigate competing causes before assigning a bottleneck.
13. Repeat, compare, and report
Preserve the workload or target-state definition, test configuration, Kubernetes version, and measurement definitions. After a code, configuration, or infrastructure change, rerun the same scenario and compare like with like. Report the observed outcomes against the criteria, include the relevant application and cluster signals, and note environmental differences that limit the comparison. Configuration-driven scenarios and measurements are central to ClusterLoader2’s approach, and the same discipline makes application load tests more useful over time.
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