UI Optimism with Mutation Variables Phase 25 - Optimistic Updates
Start with the simpler strategy Two optimistic strategies in TanStack Query
UI optimism Cache optimism Render mutation.variables while pending onMutate changes cached data No cache rewrite Snapshot + rollback required Simpler rollback story Useful across multiple consumers
Default to UI optimism when one place needs the prediction.
Use cache optimism when several observers must see the same pending change.
TanStack Query supports rendering the pending mutation variables directly. This changes what the user sees without modifying the Query cache. That means there is no cache snapshot to restore.
Status button example const updateStatus = useMutation({ mutationKey: ["tasks", "update-status", task.id], mutationFn: (status: TaskStatus) => updateTask(task.id, { status }), onSettled: () => queryClient.invalidateQueries({ queryKey: taskKeys.all, }), })
const visibleStatus = updateStatus.isPending ? updateStatus.variables : task.status
Why this is powerful • The original Query cache remains untouched.
• A failure naturally falls back to the cached server value.
• The mutation can still expose an inline error and retry action.
• This is ideal when only the component initiating the mutation needs to render the prediction.
Cross-component pending UI If another component must display the pending mutation, give the mutation a stable mutationKey and read matching pending variables with useMutationState().
UI Optimism with Mutation Variables 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 “UI Optimism with Mutation Variables” as one observable behavior. Write the success signal before touching code.
Trace the boundary map: route data → UI state → component tree → interaction → accessible feedback. 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.
if (query.isPending) return <TaskSkeleton />;
if (query.isError) return <TaskError retry={query.refetch} />;
if (!query.data.length) return <EmptyTasks />;
return <TaskTable tasks={query.data} />;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 “UI Optimism with Mutation Variables” 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.Temporary IDs colliding with real IDs
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.