Hands-On Lab and Phase Exit Phase 25 - Optimistic Updates
Lab 1 - UI optimism Convert the Task status control to render mutation.variables while pending. Do not touch the cache. Add an inline retry action on failure.
Lab 2 - Cache optimism On a Task detail page, snapshot the Task query, cancel it, patch the status in onMutate, restore it in onError, and replace it with the server response in onSuccess.
Lab 3 - Concurrency Trigger multiple status changes quickly. First observe parallel behavior. Then add a per-Task mutation scope and verify the queue preserves ordering for that Task while other Tasks remain independent.
Lab 4 - Failure drills • Force a 403 and confirm rollback + explanation.
• Force a 409 state-transition conflict and confirm rollback.
• Throttle/offline the network and observe pending UI and retry behavior.
• Keep a Task list filtered by status and verify the authoritative list after invalidation.
Exit criteria • I can explain UI optimism versus cache optimism.
• I can implement cancel -> snapshot -> patch -> rollback -> reconcile.
• I understand why list caches are harder than detail caches.
• I can reason about multiple concurrent mutations.
• I know which high-risk operations should remain pessimistic.
• I can debug an optimistic race with a timeline rather than guessing.
Sources Source Use in this phase
TanStack Query - Optimistic Updates Documents the two supported strategies: UI optimism https://tanstack.com/query/latest/docs/framework through mutation variables and cache optimism through /react/guides/optimistic-updates onMutate plus rollback context.
TanStack Query - Query Cancellation Explains cancellation and why cancelQueries is important https://tanstack.com/query/latest/docs/framework before an optimistic cache write that could otherwise be /react/guides/query-cancellation overwritten by an in-flight refetch.
TanStack Query - Invalidations from Shows targeted invalidation after successful/settled mutations Mutations and awaiting invalidation when pending state should include https://tanstack.com/query/latest/docs/framework reconciliation. /react/guides/invalidations-from-mutations
TanStack Query - Mutations Documents mutation scopes: mutations run in parallel by https://tanstack.com/query/latest/docs/framework default, while identical scope IDs serialize execution. /react/guides/mutations
TanStack Query - useMutationState Exposes pending mutation variables across components and https://tanstack.com/query/latest/docs/framework supports concurrent optimistic UI with mutation keys and /react/reference/functions/useMutationState submittedAt.
TanStack Query - QueryClient Documents get/setQueryData, cancelQueries, https://tanstack.com/query/latest/docs/reference/ invalidateQueries, getQueriesData, setQueriesData, and QueryClient cache control used by optimistic workflows.
Hands On Lab and Phase Exit 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 “Hands On Lab and Phase Exit” 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 “Hands On Lab and Phase Exit” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Simulate two rapid mutations and reason about race ordering and stale responses.
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.