Cache Optimism: onMutate, Snapshot and Rollback Phase 25 - Optimistic Updates
When cache optimism is justified If multiple components observe the same Task detail cache, changing only the initiating button is insufficient. Then TaskFlow may optimistically patch the shared detail cache.
The four-step pattern onMutate: async (status, context) => { const key = taskKeys.detail(taskId)
await context.client.cancelQueries({
queryKey: key,
})const previous = context.client.getQueryData<Task>(key)
context.client.setQueryData<Task>( key, old => old ? { ...old, status } : old, )
return { previous } }
Rollback onError: (_error, _status, rollback, context) => { if (rollback?.previous) { context.client.setQueryData( taskKeys.detail(taskId), rollback.previous, ) } }
What the snapshot means The snapshot is not a second permanent source of truth. It is temporary rollback material associated with that mutation attempt.
Deployment is the controlled movement of a versioned artifact into a configured runtime with evidence of health and a credible rollback path.
Restate the goal of “Cache Optimism onMutate Snapshot and Rollback” as one observable behavior. Write the success signal before touching code.
Trace the boundary map: versioned artifact → production config → runtime → health/smoke test → rollback path. 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.
1. Build an immutable artifact.
2. Apply production configuration/secrets.
3. Run compatible migrations.
4. Start the new version.
5. Check readiness and smoke tests.
6. Observe errors/latency.
7. Promote or execute rollback.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.
Separate platform deploy status from application readiness; run smoke tests against real routes and dependencies.
Use expand/contract migrations and keep rollback compatibility explicit before release.
Without copying the final source, create a second small scenario that applies “Cache Optimism onMutate Snapshot and Rollback” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Apply an optimistic update to the exact affected query data.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Older mutation response overwriting newer user intent
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.