Managing Async UX States

Network requests introduce complexity that synchronous code doesn’t have. Without careful planning, applications flicker between states, show stale data, and display generic error messages that don’t help users understand what happened. This guide covers how to handle async operations so users trust your application when things are slow or fail.

Plan Your Async States

Every API call goes through a predictable lifecycle. In my experience, most developers consider “loading” and “success” but overlook the other states until they cause problems in production.

• Map the state lifecycle: For each API call, define what happens when it’s idle (hasn’t started), pending (in progress), successful (completed), empty (no data returned), partial (some data but not all), or failed (error occurred). All six states matter, including the ones that seem rare.
• Group or split requests strategically: Large forms that submit everything as one request can be difficult to debug when something fails. Breaking them into smaller calls lets you show users exactly which section had a problem.
• Agree on UX principles: Work with designers before implementation to decide when to block actions during loading, when to allow background updates, and how retries should work. These conversations are more productive before code is written.

Protect Your UI with Error Boundaries

A single failed API call shouldn’t break an entire page.

• Wrap risky areas: Use Async Boundary components around sections that fetch data. When a fetch fails, the boundary catches it and shows fallback UI instead of leaving users with a blank screen.
• Add page-level boundaries: Component boundaries work well, but combine them with route-level error pages for serious failures. This provides a safety net when multiple things go wrong simultaneously.
• Track boundary triggers: Log every time a boundary activates. If a particular boundary catches errors frequently, that signals a reliability problem.

Show Clear Loading Feedback

When users don’t see feedback quickly, they often click again, creating duplicate requests and potential race conditions.

• Match loaders to context: Use Loading State patterns appropriate to the situation. Small spinners work for buttons; skeleton screens fit content areas; full-page overlays suit critical operations. The scale of the loading indicator should match the scope of what’s loading.
• Differentiate first load from refresh: Users accept skeleton screens on initial page load because they’re already waiting. When refreshing data they’ve already seen, they prefer a subtle indicator rather than watching the entire UI disappear and rebuild. Optimistic updates work well here: show the expected result immediately and correct it if the server disagrees.

Update UI Immediately with Optimistic Updates

Waiting for server confirmation before updating the UI makes applications feel sluggish. Assuming success and handling failure when it occurs creates a more responsive experience.

• Show changes immediately: Use Optimistic Updates to reflect user actions in the UI right away, before the server confirms. Preserve the previous state so you can revert if the server rejects the change. Users get instant feedback, making the application feel faster than it actually is.
• Prevent conflicts: While waiting for confirmation, either disable conflicting actions or clearly mark items as “saving.” Without this, users may repeat an action multiple times, creating duplicate requests that race to the server.

Handle Failures Gracefully

Network requests fail regularly: mobile networks drop, APIs time out, and corporate firewalls block unexpected endpoints. Planning for failure is essential.

• Implement smart retries: Use Retry strategies with exponential backoff. Wait progressively longer between each retry instead of immediately repeating failed requests. Show users when you’re retrying and how many attempts remain. Silent retries that take a long time make users think the application has frozen.
• Cancel outdated requests: Use Request Cancellation to abort old API calls when users navigate away or change search filters. Without cancellation, slow requests can complete after newer ones, causing the UI to show stale results.
• Log useful failure information: Generic error messages don’t help with debugging. Log which endpoint failed, how long it took, the error code, and whether retries succeeded. Detailed logs make production issues much easier to diagnose.

Test and Monitor Async Behavior

Async issues often hide during development and appear in production under real-world conditions.

• Test failure scenarios: Write integration tests that simulate slow responses and server errors. Verify that error boundaries, loading states, and optimistic updates work as intended. Local development environments with fast responses don’t reflect how your application behaves on slow or unreliable networks.
• Watch pending requests: Monitor how many requests are in-flight in production. If that number climbs steadily, either cancellation logic isn’t working correctly or the backend is struggling. Both are worth investigating.
• Write clear error messages: Generic messages don’t help users understand what happened or what to do next. Specific messages like “We couldn’t save your changes because the server is overloaded. We’ll retry automatically in a few seconds” are more helpful. Work with UX writers to create error messages that inform rather than confuse.

Remember: Async state management is core UX, not an edge case. Well-handled loading, errors, and retries build user trust. Poorly-handled ones erode it.