Health, Liveness, Readiness, and Graceful Shutdown
A process can be alive but not ready to serve real traffic. TaskFlow separates those concepts so deployment infrastructure can make the right decision.
critical dependencies usable; /health/live process can respond; /health/ready deploy/router sends traffic only when ready → →
@Get('live') live() { return { status: 'ok' }; }
@Get('ready')
@HealthCheck()
ready() {
return this.health.check([
() => this.prismaHealth.pingCheck('database', this.prisma),
]);
}NestJS Terminus provides readiness/liveness health-check support and includes a PrismaHealthIndicator. Enable shutdown hooks so the application can transition cleanly during termination.
• Liveness should be cheap and should not fail merely because an external dependency has a transient issue.
• Readiness may check PostgreSQL and other truly required dependencies.
• Do not put slow external calls to every third-party API into every probe.
• Health responses must not expose credentials, connection strings, internal topology, or stack traces.
Health Liveness Readiness 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 Liveness Readiness 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: Observability layer connecting Next.js/API errors and request IDs to structured logs and monitoring releases.. 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 Liveness Readiness 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.
Add a few meaningful application/HTTP/database signals rather than hundreds of vanity metrics.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.console.log strings with no searchable structure
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.