Authentication, Cache Isolation and Privacy Treat logout as a data-boundary event, not just a navigation action
Phase 22 - TanStack Query Volume 06 - Professional Frontend
Cached data can be sensitive A browser QueryClient can outlive individual route components. If user A signs out and user B signs in without a full browser reload, stale cache entries must not become an accidental cross-account disclosure.
queryClient.clear()
export async function signOutTaskFlow(queryClient: QueryClient) {
await supabase.auth.signOut({ scope: "local" });
queryClient.clear();
window.location.assign("/sign-in");
}Do query keys need the user ID? Sometimes tenant/user context belongs in a query key because it changes the requested resource. However, adding user IDs everywhere is not a substitute for clearing security-sensitive caches on auth transitions. TaskFlow treats logout as a cache lifecycle boundary.
401 handling A query that receives 401 should not retry repeatedly. The API client should surface an ApiClientError; retry policy returns false; the UI can start session recovery or redirect to sign-in. Authorization failures (403) should remain visible as permission outcomes, not auth-refresh loops.
Phase 22 does not enable localStorage persistence for QueryCache. Persisting cached business data introduces privacy, staleness and logout-cleanup responsibilities that should be justified by a concrete offline requirement.
Account-transition checklist Event Cache action
Session becomes invalid Stop retries; recover auth; clear sensitive cache if identity is lost
This is a privacy boundary as much as a correctness rule. A stale cache must never become an alternate way to view data the current identity could not retrieve from the API.
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 “Authentication Cache Isolation and Privacy” 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 “Authentication Cache Isolation and Privacy” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Add task detail query and prove list/detail caches are distinct.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Copying query data into useState/useEffect
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.