Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsA realistic API performance test starts with a decision, not a script. Decide what you need to learn, such as whether the service holds its SLO under expected traffic or where it breaks under unusual traffic. Then model the workload, pick the right arrival model, use plausible data, verify correctness, and set pass/fail thresholds before the first run. This guide follows Grafana’s k6 documentation for the mechanics. The design principles apply to any load tool, but the examples and syntax come from k6.
Start with three scoping questions
Grafana’s API load testing guide frames the opening questions well. Answer them in writing before you build anything.
- Do you want to test a single endpoint or an entire flow? A single endpoint isolates a baseline or breaking point. A flow shows how chained calls behave together.
- What flows or components do you want to test? Pick the frequent or critical scenarios first.
- What criteria determine acceptable performance? These should come from your SLOs and business or reliability goals, not from habit.
Step 1: Name the decision the test supports
Validating reliability under expected traffic is a different question from discovering limits under unusual traffic. The same script can run with different load profiles for different questions, so choose the profile after the goal is clear (Grafana Labs).
Step 2: Choose scope and grow it gradually
Begin with one API when you need its isolated baseline. Then test interactions among APIs and end-to-end flows for the scenarios users hit most or that matter most to the business. Grafana’s advice is to “Start simple and test frequently. Iterate and grow the test suite.” This is organizational guidance from Grafana Labs, and the page names no individual author. Reuse and modularize scenario code as the suite grows, rather than starting with one large, opaque scenario.
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Step 3: Describe the workload from your own evidence
Estimate or observe the following for your specific service:
- arrival rate and concurrent users,
- the mix of scenarios,
- normal peaks,
- sudden surges.
The k6 documentation explains how to configure workload shapes. It does not supply a universal production traffic mix, and no such standard is established. Use production logs, analytics, or capacity forecasts rather than an invented split such as “80% reads”.
Step 4: Pick the right scheduling model
This choice often decides whether a test is realistic.
| Aspect | Closed model | Open model |
|---|---|---|
| When an iteration starts | Only after the same virtual user finishes its previous iteration | On a schedule, independent of response time |
| When the system slows | Fewer iterations arrive, which eases the pressure | Arrivals continue, so queues and latency can build |
| Best for | Representing a fixed population of concurrent users | Holding arrivals or throughput steady while the system degrades |
| In k6 | VU-based executors | Arrival-rate executors |
Grafana’s open and closed models page warns that the closed model can cause coordinated omission when you intend to maintain an independent arrival rate. The test slows down with the server and under-reports how bad things get.
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Using constant arrival rate correctly
- The constant-arrival-rate executor starts a fixed number of iterations per time unit, provided virtual users are available.
- An iteration can make several requests. The iteration rate is therefore not the request rate, so divide your target request rate by requests per iteration.
- Preallocate enough virtual users and allow scaling, so the executor can keep to its schedule.
- Do not add an end-of-iteration sleep. The executor already paces starts.
Step 5: Make data and scripts behave plausibly
- Parameterize inputs such as user IDs and credentials, so iterations do not all behave like one hard-coded user.
- Check responses for expected status, headers, and content.
- Handle errors in dependent steps. If a login fails, the next call should not crash the script, because the crash hides how the system actually behaved.
Step 6: Set the scorecard before the run
Derive thresholds from your SLOs, then track four things. Grafana’s what k6 measures page covers the built-in metrics.
- Latency distribution. Look at the tail. The k6 learning material recommends p95 and p99 over averages for gates.
- Throughput. Track request totals and request rate.
- Errors. Set a failed-request limit that follows your reliability goal.
- Correctness. Record checks, then enforce them through thresholds. Fast but wrong responses are still failures.
The numbers in Grafana’s guide are examples only. One example sets an error rate below 1% and p95 request duration below 200 ms. Another has 99% of product-information API calls responding within 600 ms. Neither is a recommended universal target, and the page gives no publication year. Your own SLOs set the numbers.
Step 7: Validate the test environment
Choose where load generators run based on your test requirements and location. Confirm the generator can sustain the intended schedule. If k6 cannot start iterations on time because it ran out of virtual users or machine capacity, that is a test problem, not an API problem. Check generator health before blaming the service.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Step 8: Match the profile to the question
| Profile | Purpose |
|---|---|
| Smoke | Confirm basic function with minimal load |
| Typical traffic | Validate expected operation |
| Stress / peak | Assess behavior at peak traffic |
| Spike | Test abrupt increases |
| Breakpoint | Find the limits |
These profiles come from the Grafana guide. You can also compare them by scope (endpoint, integrated APIs, end-to-end flow) and by where and how much load you can generate.
Pre-run checklist
- The decision the test supports is written down.
- Scope is chosen, and the workload comes from your own traffic evidence.
- Open or closed scheduling matches the question being asked.
- Requests per iteration are accounted for in the rate target.
- Data is parameterized and checks cover status, headers, and payload.
- Thresholds on tail latency, errors, and checks come from SLOs.
- The generator can sustain the schedule.
What the sources do not settle
The mechanics here come from current Grafana k6 documentation (the pages showed v2.3.x when accessed on 2026-10-05). They do not establish a standard realistic traffic mix or universal thresholds. They also do not compare k6 with JMeter, Gatling, or Locust. Teams whose tests outgrow local execution can look at hosted options such as Grafana k6.
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