Query Keys and Query Options Design cache identity before writing hooks
Phase 22 - TanStack Query Volume 06 - Professional Frontend
A query key is the identity of cached data If two requests can return different data, they must not accidentally share the same key. TanStack Query requires top-level array keys and hashes serializable key contents deterministically.
export const taskKeys = { all: ["tasks"] as const, lists: () => [...taskKeys.all, "list"] as const, list: (query: TaskListQuery) => [...taskKeys.lists(), query] as const, details: () => [...taskKeys.all, "detail"] as const, detail: (id: string) => [...taskKeys.details(), id] as const, };
Variables used by queryFn belong in the key taskKeys.list({ page: 2, pageSize: 20, status: "IN_PROGRESS", search: "payroll", })
Page, search, status, priority and sort affect the returned collection, so they belong in the list key. If you omit one, the cache can return data produced under different inputs.
Use queryOptions for reusable contracts import { queryOptions } from "@tanstack/react-query";
export function taskListOptions(query: TaskListQuery) { return queryOptions({ queryKey: taskKeys.list(query), queryFn: () => fetchTasks(query), staleTime: 30_000, }); }
queryOptions keeps the key and fetch function together. The same definition can later be used by useQuery, prefetchQuery and cache operations.
Keys are cache identifiers and debugging surfaces. Never include bearer tokens, refresh tokens, passwords or secrets. Authentication is transport context, not data identity.
Prefix invalidation The key factory makes broad-but-targeted invalidation easy: invalidating taskKeys.lists() refreshes every cached Task collection without also invalidating unrelated resources.
TanStack Query is a server-state synchronization system, not merely a fetch helper. Cache identity, freshness, invalidation, and mutation semantics are the core model.
Restate the goal of “Query Keys and Query Options” as one observable behavior. Write the success signal before touching code.
Trace the boundary map: component → query key → cache state → fetch/mutation → reconciliation. 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: TaskFlow query/mutation layer with stable keys, list/detail caching, invalidation, filters, and debugging via devtools/logs.. 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.
export const taskKeys = {
all: ["tasks"] as const,
list: (filters: TaskFilters) => ["tasks", "list", filters] as const,
detail: (id: string) => ["tasks", "detail", id] as const,
};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 “Query Keys and Query Options” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Move task-list fetching into query options and observe loading/error/success/cache states.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Invalidating too broadly or not at all
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.