Lab A - Establish baseline • Run Next.js production build, inspect bundle output, and record Core Web Vitals lab measurements.
• Capture p50/p95/p99 for GET /tasks and PATCH /tasks/:id under a fixed autocannon scenario.
• Record CPU/memory and PostgreSQL connection/query signals during the run.
Lab B - Database investigation • Use a realistic Task dataset.
• Capture EXPLAIN (ANALYZE, BUFFERS) for the most common filtered/sorted Task query.
• Identify one index candidate based on real predicates/order.
• Apply it in a migration, ANALYZE, rerun the same plan and load test, then keep or revert based on evidence.
Lab C - Frontend investigation • Enable the bundle analyzer.
• Find one large client dependency or unnecessarily broad client boundary.
• Apply one targeted optimization and compare production build/bundle output.
• Verify functionality and accessibility did not regress.
Phase exit checklist Question Exit requirement
Do we know our budgets? Yes - documented per layer
Can we reproduce benchmarks? Yes - scenario, dataset, build, concurrency, duration recorded
Can we inspect a slow SQL plan? Yes - EXPLAIN/ANALYZE/BUFFERS used safely
Can we detect N+1? Yes - query count/trace/repository review
Is pooling intentional? Yes - topology and connection budget documented
Do we optimize after measuring? Yes - benchmark before/after, keep or revert
Key idea: Phase 31 is complete when the learner can explain why a system is slow, prove the bottleneck with evidence, change the smallest useful boundary, and demonstrate the improvement with the same measurement.
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: baseline → measurement → bottleneck → targeted change → re-measurement. 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: Before/after performance report with measured bottleneck, change, and regression guard.. 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 /tasks p95: 1.8 s
API application: 90 ms
database: 1.61 s
serialization/network: 100 ms
Next action: inspect query plan before changing frontend code.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.
Record the before/after result and add a budget/test where a regression would matter.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Benchmarking development mode and assuming production behavior
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.