TanStack Query Testing and Cache Isolation TaskFlow - Full Stack Developer 2026
Never share a QueryClient across tests function createTestQueryClient() { return new QueryClient({ defaultOptions: { queries: { retry: false }, mutations: { retry: false }, }, }) }
Why retries are disabled in tests Application retry behavior has dedicated tests. Disabling default retry loops makes ordinary failure tests deterministic and prevents “expected error” tests from waiting through backoff.
Optimistic update scenarios from Phase 25 • Prediction appears immediately.
• Server success reconciles with authoritative Task.
• 403/409 restores rollback snapshot.
• Old in-flight query cannot overwrite the optimistic cache after cancellation.
• Same-Task serialized mutations preserve ordering.
• Query invalidation happens at the intended key scope.
Network boundary Mock the HTTP API boundary for component/query tests rather than mocking TanStack Query itself. The thing under test is how TaskFlow uses Query behavior, not whether TanStack Query can cache data.
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 “TanStack Query Testing and Cache Isolation” 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: Vitest + integration + Playwright test suite with deterministic fixtures and CI-ready commands.. 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 “TanStack Query Testing and Cache Isolation” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Write a pure unit test for one deterministic business policy.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.E2E used for every rule, making feedback too slow
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.