Case 1 - GET /tasks p95 jumps from 220 ms to 950 ms Trace shows 780 ms in PostgreSQL. Query logs reveal a new assignee lookup per Task row. Fix the N+1 query shape, rerun integration tests, EXPLAIN the new SQL, then repeat the same load scenario. Do not start by memoizing React components.
Case 2 - API is 90 ms but page LCP is 4.1 s Bundle analysis reveals a large chart/editor library imported through a high-level client shell even though the Tasks screen does not use it initially. Move the client boundary lower and lazy-load the feature. Confirm with production build and field/lab browser measurements.
Case 3 - latency rises only at concurrency 100 Single-request traces look healthy. Under load, DB connection wait time rises and API p99 explodes. Review replica count, application pool size, Supabase pooler mode, Postgres max connections, query duration, and CPU/I/O saturation. More connections are not automatically the fix.
Case 4 - pagination gets slower on OFFSET work grows with depth. Keep offset pagination for ordinary admin tables, but use cursor/keyset pagination for deep sequential feeds where jumping to arbitrary page numbers is not required. Key idea: Performance debugging is hypothesis-driven: locate the boundary first, then optimize that boundary. The stack is too large for random tuning to be reliable.
Performance Debugging Case Studies 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 “Performance Debugging Case Studies” 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 “Performance Debugging Case Studies” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Reduce N+1 or redundant requests before adding caches.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Adding indexes that slow writes without helping real queries
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.