Collection API Mental Model Treat filters, search, sorting and pagination as one public query contract
Phase 15 - Filtering, Search, Sorting & Pagination Volume 04 - Professional Backend
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Phase 15 turns an unbounded collection endpoint into a validated, queryable, stable, paginated API contract.
A collection endpoint is a query interface GET /tasks is not just an array-returning function. It is a public query interface between clients and a dataset. A professional contract needs explicit choices about scope, filters, search, order, pagination, defaults, limits, and response metadata.
Scope Which Tasks may this endpoint expose?
Filter Which exact attributes narrow the collection?
Search Which text fields participate and with what semantics?
Sort Which fields may control ordering?
Stability How are ties broken?
Pagination How many rows may one request return?
Metadata How will a UI know whether more pages exist?
Query parameters express collection state Collection choices normally belong in the URL because they describe which representation of the collection the client wants. This makes URLs bookmarkable, shareable, inspectable in DevTools, and repeatable during debugging.
/tasks?status=IN_PROGRESS&priority=HIGH&page=2&pageSize=20
Avoid endpoint explosion Do not create one route for every filter combination such as /high-priority-tasks, /completed-tasks, /my-high-priority-tasks. Keep the resource stable and express queryable collection state with validated parameters.
/tasks/high-priority /tasks?priority=HIGH
/tasks/completed /tasks?status=DONE
/searchTasks?q=x /tasks?search=x
/tasks/page/2 /tasks?page=2
A query parameter can ask for projectId or assigneeId, but it must never be trusted as proof that the caller is allowed to see that scope. Authorization arrives later and will intersect with these filters.
Collection API Mental Model 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 “Collection API Mental Model” as one observable behavior. Write the success signal before touching code.
Trace the boundary map: query parameters → validated filter/sort → stable query → page/cursor → response metadata. 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: Task list API with filter/search/sort/page contract plus metadata and indexes justified by measured query patterns.. 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.
orderBy: [{ createdAt: "desc" }, { id: "desc" }],
take: limit + 1,
where: cursor ? {
OR: [
{ createdAt: { lt: cursor.createdAt } },
{ createdAt: cursor.createdAt, id: { lt: cursor.id } },
],
} : undefinedDo 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 “Collection API Mental Model” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Write the query contract including defaults and maximum page size.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Unbounded page size
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.