Server Reconciliation: onSuccess, onSettled and Invalidation Phase 25 - Optimistic Updates
Optimism ends when the server responds The local prediction must eventually be replaced or confirmed by server truth. TaskFlow uses the returned Task when useful, then invalidates affected lists so authorization, filtering, derived state, timestamps, and business rules are re-evaluated by the backend.
Reconcile detail immediately onSuccess: (serverTask, _status, _rollback, context) => { context.client.setQueryData( taskKeys.detail(taskId), serverTask, ) }
Then invalidate authoritative collections onSettled: async (_data, _error, _status, _rollback, context) => { await Promise.all([ context.client.invalidateQueries({ queryKey: taskKeys.detail(taskId), }), context.client.invalidateQueries({ queryKey: taskKeys.lists(), }), ]) }
Awaiting invalidation TanStack Query keeps the mutation pending while an awaited callback Promise is unresolved. That can be useful when the UI should not announce “saved” until reconciliation has started/completed according to your policy.
Server Reconciliation onSuccess onSettled and Invalidation matters because it changes a real boundary in TaskFlow. The goal is to understand the inputs, owner, observable output, and failure modes before adding more abstraction.
Restate the goal of “Server Reconciliation onSuccess onSettled and Invalidation” as one observable behavior. Write the success signal before touching code.
Trace the boundary map: untrusted input → runtime schema → validated value → business rule → safe operation. 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: Optimistic task update/create flow with rollback and authoritative server reconciliation.. 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.
const CreateTaskSchema = z.object({
title: z.string().trim().min(1).max(160),
priority: z.enum(["LOW", "MEDIUM", "HIGH"]),
});
const input = CreateTaskSchema.parse(requestBody);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 Reconciliation onSuccess onSettled and Invalidation” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
On success/settle, reconcile with authoritative server data rather than trusting the optimistic object forever.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Optimism without rollback
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.