Health, Readiness, Zero-Downtime Promotion, and Graceful Shutdown A process can be alive but not ready. Deployment platforms should route traffic only after the new version is ready, and the old version should have time to finish work during termination.
Start new version → /health/ready 2xx → Switch traffic → SIGTERM old → Graceful exit
Endpoints • /health/live: process is alive; avoid making it fail because PostgreSQL is briefly unavailable.
• /health/ready: operation-critical dependencies such as PostgreSQL are available.
• Never require user authentication for platform health probes.
Platform behavior • Railway waits for a successful 2xx healthcheck before making the new deployment active.
• Render can also use HTTP health checks to decide whether a new web-service deployment is ready for traffic.
• Phase 30 observability still monitors health after rollout; deployment probes are not a substitute for alerts. Readiness gates traffic. Liveness decides whether the process itself should be considered alive. Mixing them creates unnecessary restarts during dependency incidents.
Sources: Railway deployment healthchecks and Render HTTP healthchecks.
Health Readiness Zero Downtime and Graceful Shutdown matters because it changes a real boundary in TaskFlow. The goal is to understand the inputs, owner, observable output, and failure modes before adding more abstraction.
Restate the goal of “Health Readiness Zero Downtime and Graceful Shutdown” as one observable behavior. Write the success signal before touching code.
Trace the boundary map: request → correlation id → structured event → metric/trace → diagnosis. Mark the one boundary this lesson changes.
Inspect the current repository state and record the exact files, route, table, or runtime involved. Avoid creating an abstraction before you can name the existing owner.
Implement the smallest vertical slice. Add one coherent change, run the narrowest verification command, and read the output before continuing.
Connect the slice to TaskFlow’s phase artifact: Staging and production deployment runbooks with release identity, environment matrix, smoke tests, and rollback procedure.. Verify behavior from the outside as a user or API client would.
Create one controlled failure that is safe to reproduce. Predict the symptom first, capture evidence, locate the earliest broken boundary, then fix only that cause.
Close the reference and rebuild or explain the core change from memory. Record what you would monitor or test before trusting the same change in production.
GET /health/live -> process is running
GET /health/ready -> required dependencies can serve trafficDo not copy this as a finished answer. Predict what each boundary does, type the smallest useful slice, run it, then compare the result with your prediction.
Return to the nearest observable boundary: input, network, process log, database, or rendered UI. Compare actual evidence with the predicted path.
Change one variable at a time and reproduce the original failure after the fix; otherwise you have correlation, not diagnosis.
Without copying the final source, create a second small scenario that applies “Health Readiness Zero Downtime and Graceful Shutdown” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Deploy Next.js from the intended monorepo root with public/server env vars separated.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Rollback plan assumes schema can always be downgraded
Predict the symptom before causing the failure. Then locate the earliest boundary where reality diverges from expectation.Why does this responsibility live here, what input does it trust, what output proves success, and what would go wrong if another layer owned it?
If your explanation is vague, repeat the boundary trace before moving on.Close the reference. Reproduce the core idea from memory or explain the exact request/data flow aloud. Then check your diff or notes for accidental shortcuts that bypass validation, authorization, error handling, or verification earned in earlier phases.