Level 1 - Follow • Enable JSON logging in production mode.
• Add a request completion interceptor/middleware with requestId, route, status, and duration.
• Expose liveness and readiness endpoints.
Level 2 - Integrate • Install/configure @nestjs/terminus 12.0.0 and wire PostgreSQL readiness.
• Add @sentry/nestjs 10.75.0 and @sentry/nextjs 10.75.0 in non-production first.
• Propagate requestId into error monitoring.
Level 3 - Observe • Create request-rate, error-rate, and latency views.
• Record a deliberate 500 and trace it from response requestId to logs/error monitor.
• Simulate PostgreSQL outage and verify liveness versus readiness behavior.
Level 4 - Engineer Design an observability plan for a payroll finalization workflow. Identify the logs, metrics, trace spans, health dependencies, alert condition, and sensitive data that must never be recorded.
Phase exit criteria • A production request can be correlated across public error response, logs, and error monitoring.
• TaskFlow has explicit liveness and readiness semantics.
• Latency is measured as a distribution, not only an average.
• Metrics avoid high-cardinality labels.
• Telemetry has a written redaction/privacy policy.
• Dashboards and alerts answer operational questions and have owners/runbooks. Key idea: Phase 30 is complete when an engineer can diagnose a realistic failure from emitted evidence without adding emergency console.log statements.
Hands On Lab and Phase Exit 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 “Hands On Lab and Phase Exit” 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.
logger.error({
requestId, route: request.url, userId: user?.id,
errorCode: error.code, durationMs,
}, "request failed");Do 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 “Hands On Lab and Phase Exit” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Simulate a production-style error and walk from user-visible request ID to logs/monitoring evidence.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.No release/environment tags so errors cannot be tied to deployments
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.