Concurrent Mutations and Mutation Scopes Phase 25 - Optimistic Updates
Mutations run in parallel by default Two rapid clicks can produce two independent update requests. If they target the same Task, response order may differ from intent order. That means a naive optimistic implementation can show stale or surprising final state.
Serializing one Task status stream const updateStatus = useMutation({ mutationKey: ["tasks", "status", taskId], scope: { id: `task-status:${taskId}`, }, mutationFn: (status: TaskStatus) => updateTask(taskId, { status }), // optimistic callbacks... })
What scope does TanStack Query documents that mutations run in parallel by default. Mutations with the same scope.id execute serially, with later ones queued while an earlier one is running.
Do not serialize unrelated work Different Tasks should normally remain independent. Use the smallest scope that matches the resource/workflow whose ordering must be preserved.
Global pending indicators Use useMutationState() or useIsMutating() with mutation keys for focused “Saving…” indicators rather than a hand-built global boolean.
Concurrent Mutations and Mutation Scopes 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 “Concurrent Mutations and Mutation Scopes” as one observable behavior. Write the success signal before touching code.
Trace the boundary map: user action → snapshot → optimistic cache → server result → commit/rollback. 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.
onMutate: async (input) => {
await queryClient.cancelQueries({ queryKey: taskKeys.all });
const previous = queryClient.getQueryData(taskKeys.all);
applyOptimisticChange(input);
return { previous };
},
onError: (_err, _input, ctx) => restore(ctx?.previous),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 “Concurrent Mutations and Mutation Scopes” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Capture the previous cache state before changing it.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Optimistically updating the wrong filtered list
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.