Explain Delete and Error Semantics without relying on framework buzzwords.
Connect the lesson to the phase outcome: Build persistent task CRUD through controller → service → Prisma with explicit contracts and verified database effects.
Reproduce the relevant behavior and verify it with evidence.
Diagnose at least one representative failure instead of guessing.
Figure 11.7 — TaskFlow boundary map
HTTP route
→
controller
→
service
→
Prisma query
→
PostgreSQL
→
mapped response
This lesson belongs to Phase 11. Keep the full request/data path visible even when today's code changes only one box.
Instructor workflow: predict first → implement the smallest slice → run it → inspect evidence → break one assumption → diagnose → explain it back. Do not copy the whole code block before understanding the first failing boundary.
Delete and Error Semantics
204 deletion, missing rows, and honest HTTP meaning
Phase 11 replaces process memory with durable PostgreSQL-backed application behavior.
DELETE /tasks/:id - remove a durable row
Deletion is a useful place to separate database return values from HTTP response semantics. Prisma can
return the deleted row internally, while the HTTP endpoint can intentionally return 204 No Content.
204 means the request succeeded and there is intentionally no response body. A later GET for the same id should
return 404.
Error mapping is not a global architecture yet
Phase 11 handles the one Prisma error needed for CRUD semantics close to TasksService. Phase 13 will
introduce a more systematic error strategy and exception mapping. We do not build a giant global abstraction
before there is enough repeated behavior to justify one.
Situation HTTP meaning Current handling
Task does not exist 404 Not Found NotFoundException
Create succeeds 201 Created Nest POST default
Read/update succeeds 200 OK Nest default
Delete succeeds 204 No Content Explicit @HttpCode
Malformed body Not robust yet Phase 12
Unexpected database failure 500-class failure Propagates to Nest
Do not turn every database error into 400
A timeout, connection failure, constraint violation, and missing row are not the same condition. Error handling
should preserve meaning. If the application cannot classify an unexpected failure safely, it should not fabricate
a friendly-but-wrong status code.
Executable lesson studio · errors
Now turn the explanation into engineering skill.
Delete and Error Semantics 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.
By the end, you should be able to
Explain Delete and Error Semantics in terms of concrete inputs, ownership, outputs, and evidence.
Implement or reproduce the smallest TaskFlow slice that demonstrates this errors concept.
Diagnose one representative errors failure using observable evidence instead of trial-and-error.
Figure 11.7B — Mental model
failure
→
typed/domain error
→
exception mapping
→
HTTP envelope
→
client handling
Read left-to-right while implementing. When debugging, walk right-to-left until the first observed fact differs from your prediction.
Code-along sequence
One coherent change at a time
01
Restate the goal of “Delete and Error Semantics” as one observable behavior. Write the success signal before touching code.
02
Trace the boundary map: failure → typed/domain error → exception mapping → HTTP envelope → client handling. Mark the one boundary this lesson changes.
03
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.
04
Implement the smallest vertical slice. Add one coherent change, run the narrowest verification command, and read the output before continuing.
05
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.
06
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.
07
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.
jsonStable client-facing error envelope
{
"error": {
"code": "TASK_NOT_FOUND",
"message": "Task was not found",
"requestId": "req_..."
}
}
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.
Verification
Prove the happy path
Run the narrowest relevant command or request and require a clean exit/status.
Inspect the boundary-specific evidence: terminal output, Network panel, database row/query plan, test report, container health, or deployment logs.
Repeat the behavior once after the change so a one-off cached or stale result cannot masquerade as success.
Break it on purpose
Build debugging memory
Missing records bubble up as raw ORM exceptions
Change one assumption in the errors example—identity, input, configuration, ordering, connectivity, or state—and predict the exact symptom before running it.
SymptomInvestigation path
Expected result does not appear
Return to the nearest observable boundary: input, network, process log, database, or rendered UI. Compare actual evidence with the predicted path.
A shortcut appears to fix it
Change one variable at a time and reproduce the original failure after the fix; otherwise you have correlation, not diagnosis.
Closed-reference challenge
Can you transfer the idea?
Without copying the final source, create a second small scenario that applies “Delete and Error Semantics” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Explain-back
Answer these without reopening the solution
What problem does Delete and Error Semantics solve in TaskFlow, and what would be observably wrong without it?
Which part of the errors boundary owns the decision, and which neighboring layer must not own it?
What evidence proves the happy path instead of merely suggesting it worked?
Name one realistic failure or abuse case and the first place you would inspect.
If requirements doubled tomorrow, which part of this design should stay stable and which part is allowed to change?
Lesson studio
Turn the reading into skill
01
Do it
Define Task create/update/read response shapes before implementation.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.02
Break it safely
Missing records bubble up as raw ORM exceptions
Predict the symptom before causing the failure. Then locate the earliest boundary where reality diverges from expectation.03
Explain it back
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.
Exit ticket
Before marking complete
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.