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Goal We need to load‑test an API that performs non‑idempotent POSTs while still allowing transient failure retries. Constraint Retries must not cause duplicate side‑effects; the API only guarantees idempotence when an idempotency key header is present. Unresolved Decision Should we rely on Gatling’s retry DSL combined with manual idempotency‑key handling, or
Goal: Execute k6 load‑test scripts that read credentials from environment variables so that no secret is stored in the script file, while running against a staging or mock integration that mirrors production behavior. Concern: k6 injects variables into the JavaScript runtime but provides no automatic redaction; if a script unintentionally logs or outputs __E
k6 Test Assertion Scope The primary goal is to confirm that assertions within a k6 script correctly validate application behavior during a load test. Constraint k6 does not manage external data persistence or backup operations. Does the assertion mechanism support cross-request state validation against an external data source? Which standard output format be
Gatling HTTP baseUrl configuration We configure the HTTP protocol in our Gatling simulations with .baseUrl(...) , and the same simulation runs successfully on developer machines but fails when executed in our production container and CI agent environments. The simulations target Gatling 3.x with the Java DSL. Observed constraint The value being passed resemb
Cost‑efficient injection for low‑traffic Gatling scenarios The objective is to lower cloud instance‑hour expenses for Gatling tests that simulate a low‑traffic workload, such as one user generating requests at one per second. Constraints include using Gatling 3.10+ DSL helpers, accounting for JVM warm‑up and garbage‑collection pauses that may affect resource
Goal I want a single k6 test run that exercises both layers of a web application: protocol-level HTTP scenarios driving backend load, and browser-module scenarios (Chromium via CDP) capturing frontend behavior, so one end-of-test summary contains both http_req_duration and the browser_web_vital_* metrics. Constraints and uncertainty The browser module runs e
k6's documented execution lifecycle includes a teardown() function that runs after all VUs finish. That makes teardown a natural place to drop temporary schema objects or restore a prior schema state when a load test creates database artifacts. The uncertainty is what happens when the test process does not stop gracefully. Normal SIGINT/SIGTERM handling is d
Understanding VU ramp‑down timing between stages The goal is to predict the exact moment k6 begins to reduce the number of virtual users after a stage ends, so that low‑traffic workloads can stay within the free‑tier VU‑minute quota. The documentation defines each stage by a target VU count and a duration, but it does not state whether the ramp‑down starts i
The goal is to hold a target request rate while keeping the Gatling load generator's own heap bounded, but the two documented injection strategies pull in opposite directions. A closed-workload profile such as constantConcurrentUsers(n).during(d) combined with throttle(...reachRps(r)...) keeps N sessions resident for the entire run, so memory scales with con
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