Persistence Verification and Database Inspection Prove durability with restart tests, Prisma Studio, and SQL
Phase 11 - Persistent Task CRUD API Volume 03 - Database Engineering
Architecture milestone Create database row
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Phase 11 replaces process memory with durable PostgreSQL-backed application behavior.
The restart test - proof of persistence The strongest Phase 11 demonstration is intentionally simple: create a Task, stop NestJS, start NestJS again, and read the same Task. If it survives, storage lifetime has moved out of the Node.js process and into PostgreSQL.
Figure 1 - Persistence proof POST /tasks
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row committed to PostgreSQL
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stop NestJS
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start NestJS
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same row still exists
Persistence is observable behavior, not a diagram label.
Verification sequence • Create at least two Tasks with POST /tasks.
• Record one Task id and its updatedAt value.
• Stop the API process with Ctrl+C.
• Start the API again from apps/api using pnpm run start:dev.
• Call GET /tasks and GET /tasks/:id.
• Confirm the same ids and values remain.
Inspect through Prisma Studio From apps/api you can open a development inspection UI:
pnpm exec prisma studioUse Studio as an inspection/development tool, not as a substitute for application rules. The important question is whether the Task table contains the same rows the API returns.
Inspect through Supabase The Supabase Table Editor or SQL Editor gives a second perspective. Querying the table directly helps separate database truth from API rendering problems.
select id, "projectId", title, status, priority, "createdAt", "updatedAt" from "Task" order by "createdAt" desc;
If the row exists in PostgreSQL but GET /tasks does not return it, debug the NestJS/Prisma query path. If the row never reached PostgreSQL, debug the create request or database write path.
Database constraints still matter Try to reason about what happens if a Task references a Project that does not exist. The foreign key protects relational integrity even if application code contains a bug. Application checks improve semantics; database constraints protect durable truth.
Persistence Verification and Database Inspection 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 “Persistence Verification and Database Inspection” as one observable behavior. Write the success signal before touching code.
Trace the boundary map: HTTP contract → controller → service rule → Prisma operation → database row. 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: Working /tasks API supporting create/list/read/update/delete against PostgreSQL with consistent error behavior.. 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.
Input -> owner -> transformation -> observable output -> failure evidenceDo 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 “Persistence Verification and Database Inspection” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Implement create through the service and verify both HTTP response and database row.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.UI is considered proof without inspecting API/database
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.