Performance And Load Testing Fullstack
Use When
- Latency or scale issues under peak load
- Major release affects critical journeys
Workflow
- Define workload models per persona
- Script API and browser-level tests
- Run baseline and regression comparisons
- Identify bottlenecks and tune autoscaling
- Document capacity headroom and limits
Required Checks
- SLO targets met under expected peak
- Error rate within budget during test
- Autoscaling responds within target time
- Results attached to release decision
Decision Framework
- Prefer explicit contracts and compatibility rules before implementation.
- If dependency risk is high, require timeout, retry, and fallback strategy per call path.
- If async messaging is used, require idempotency, replay, and dead-letter handling.
- If traffic patterns are volatile, require load, failover, and scaling validation before ship.
Common Rationalizations And Rebuttals
- "Retries will handle failures automatically." -> Unbounded retries can amplify outages; use budgets.
- "We can skip runbooks for now." -> Operational ambiguity delays incident recovery.
- "Contract changes are minor." -> Small breaking changes cause broad downstream regressions.
Evidence Pack
- Contract compatibility note and migration strategy (if applicable)
- Failure-mode test evidence for dependency degradation and recovery
- Observability snapshot (latency, error, saturation, or queue health)
- Rollout and rollback steps with clear trigger thresholds
Exit Criteria
- Workflow is production-ready with verified evidence
- Operational and security guardrails are in place
- Release and rollback expectations are documented
