TanStack Query Network and Render Efficiency
TanStack Query already provides structural sharing and tracked-property render optimizations. TaskFlow should tune freshness and invalidation based on how quickly each resource actually becomes stale rather than globally increasing staleTime to hide network activity.
Component rerenders on unused query state Avoid object-rest destructuring that defeats tracked-property optimization
const taskListOptions = (query: TaskListQuery) => queryOptions({ queryKey: taskKeys.list(query), queryFn: () => fetchTasks(query), staleTime: 30_000, placeholderData: keepPreviousData, });
A cache is not free memory. Large result sets cost JSON transfer, parsing, reconciliation, and browser memory even if they avoid another request. Pagination and narrow API response shapes are still primary performance tools. Production caution: Do not increase staleTime simply to make a network panel look quiet. That trades freshness for fewer requests and must match the product requirement.
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 Network and Render Efficiency” 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: Before/after performance report with measured bottleneck, change, and regression guard.. 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 Network and Render Efficiency” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Measure API endpoint latency with enough context to separate application and database time.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Adding indexes that slow writes without helping real queries
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.