Update Task Validation PATCH semantics, optional fields, nullable values, enums, and empty-update rejection
Phase 12 - DTOs, Zod & Input Validation Volume 04 - Professional Backend
Architecture milestone PATCH body
↓
updateTaskSchema
↓
service
↓
Phase 12 introduces an explicit runtime validation boundary before controller and service logic.
PATCH /tasks/:id - partial updates have different semantics Create and update inputs are related but not identical. POST requires the fields needed to create a new Task. PATCH represents a partial change to an existing Task.
projectId required not editable in this phase
title required optional
description optional optional / nullable
status defaulted by DB/domain optional enum update
priority defaulted by DB/domain optional enum update
Valid partial update { "status": "IN_PROGRESS", "priority": "HIGH" }
Invalid enum { "status": "ALMOST_DONE" }
The API vocabulary is the domain enum. A typo or invented state should be rejected before Prisma receives it.
Empty PATCH body {}
Every property is optional, but the object itself must contain at least one supported field. The refinement rule rejects a no-op PATCH.
Clearing a nullable field { "description": null }
Because null is explicitly allowed, this request means “clear the description.” Omitting description means “leave the current description unchanged.”
Why not derive update with .partial() blindly? Zod can derive a partial object, and that is useful. But update contracts often diverge from create contracts: immutable fields may disappear, different fields may become editable, and empty updates may need rejection. Explicit update schemas can be easier to reason about in business APIs.
Prefer a schema that expresses the real HTTP contract over maximum schema cleverness. Reuse small field schemas such as taskTitleSchema when it improves consistency without hiding meaning.
Update Task Validation 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 “Update Task Validation” as one observable behavior. Write the success signal before touching code.
Trace the boundary map: untrusted input → runtime schema → validated value → business rule → safe operation. 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: Reusable create/update/query schemas integrated into NestJS request handling with useful validation errors.. 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.
const CreateTaskSchema = z.object({
title: z.string().trim().min(1).max(160),
priority: z.enum(["LOW", "MEDIUM", "HIGH"]),
});
const input = CreateTaskSchema.parse(requestBody);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 “Update Task Validation” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Send malformed requests and verify the error contract is actionable but does not expose internals.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Duplicating client and server validation rules without a sharing strategy
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.