Server State and Cache Mental Model Know what TanStack Query owns - and what it does not
Phase 22 - TanStack Query Volume 06 - Professional Frontend
Four different kinds of frontend state A common React mistake is to place every changing value into component state. TaskFlow now has enough complexity that this becomes harmful. The important question is not “does this value change?” but “who owns the truth?”
Server state Task rows, total count, Task detail NestJS/PostgreSQL; cached by TanStack Query
fetch into useEffect query describes remote data
copy response into useState cache stores last result
manual loading flag URL describes collection view
manual refresh everywhere form owns unsaved input
Cache is not the database The query cache is a performance and synchronization layer. It can become stale. It can be cleared. It can be garbage collected. PostgreSQL remains authoritative. This means the application should be designed to tolerate refetching rather than treating cached objects as permanent local records.
Queries versus mutations A query reads asynchronous data. A mutation performs a server-side effect such as create, update, delete, approve or assign. A POST can technically be used as a query if it is only a read transport, but TaskFlow follows HTTP semantics and uses mutations for writes.
Avoid copying query.data into useState just to render it. Doing so creates two sources of truth and makes refetches harder to reason about.
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 “Server State and Cache Mental Model” 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 “Server State and Cache Mental Model” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Install/configure one QueryClient at the correct client boundary.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Random query-key arrays scattered through components
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.