TaskFlow already uses Fastify request.id. Phase 30 formalizes it as correlation context and returns x-request-id to clients. The same identifier belongs in logs and error-monitoring metadata so a support ticket can move directly from a user-visible request ID to internal evidence.
x-request-id / downstream Sentry response Browser Fastify Nest logs API/storage tag/context x-request-id request.id events → → → → →
const requestId = request.id; reply.header('x-request-id', requestId);
logger.log('Request completed', { requestId, method: request.method, route: request.routeOptions?.url ?? request.url, statusCode: reply.statusCode, durationMs, });
Use normalized route templates where possible. Logging `/tasks/8c1...` as a route dimension is less useful than `/tasks/:id` and creates cardinality/noise. Key idea: Correlation ID is not authentication and should not carry secrets. It is only a join key across telemetry.
Correlation IDs and Request Context 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 “Correlation IDs and Request Context” 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 “Correlation IDs and Request Context” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Redact secrets and PII deliberately before adding rich context.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Alerts on every exception rather than user-impacting symptoms
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.