useQuery, Freshness, Retries and Background Refetch Read server state without recreating fetch lifecycle by hand
Phase 22 - TanStack Query Volume 06 - Professional Frontend
useQuery is a state machine for one remote dependency const tasksQuery = useQuery(taskListOptions(query));
if (tasksQuery.isPending) return <TaskListSkeleton />; if (tasksQuery.isError) return <TaskListError error={tasksQuery.error} />;
return <TaskTable tasks={tasksQuery.data.data} />;
Pending is not the same as fetching Signal Meaning
isPending No successful data is available yet
isFetching A fetch is currently running, including background refetch
isError The query reached an error result
dataUpdatedAt When successful cached data last changed
This lets the page keep showing useful data during a background refresh instead of replacing the whole table with a spinner.
function shouldRetry(failureCount: number, error: unknown) { if (error instanceof ApiClientError) { if ([400, 401, 403, 404, 409].includes(error.status)) return false; } return failureCount < 2; }
Retrying an authorization failure does not create permission. Retrying malformed input does not fix it. Transient network and server failures are better retry candidates.
Do not flash the whole page back to a first-load skeleton every time isFetching becomes true. A subtle refresh indicator can communicate activity while preserving the last successful result.
Think in freshness transitions A successful query can be fresh, later become stale, remain visible while a refetch runs, and then receive a newer successful value. Stale does not mean unusable. It means TanStack Query may seek a newer answer according to its configured triggers.
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 “useQuery Freshness Retries and Background Refetch” 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 “useQuery Freshness Retries and Background Refetch” 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.Treating staleTime as data lifetime/deletion
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.