I jumped straight into fetching both endpoints in parallel with Promise.all, which felt right, then building a lookup map from userId to display name before rendering.
Start by fetching both endpoints in parallel, then join the data client-side using authorId. Render each post with the resolved author name, substituting 'you' when the authorId matches the logged-in user's ID. Discuss trade-offs like caching, error handling, and scalability.
Pro tip: Mention that you'd normalize the data into a map for O(1) lookups and consider using React Query or SWR for caching and deduplication, which shows production awareness.
Use Promise.all to fetch posts and users simultaneously, minimizing latency. Handle loading and error states for both requests.
Create a lookup map of users by ID (e.g., using reduce) to avoid O(n²) nested loops when matching authorId to user.
For each post, look up the author name from the map. If the authorId equals the logged-in user's ID, display 'you' instead of the name.
Consider caching, pagination, or server-side joins for large datasets. Handle missing users (e.g., deleted accounts) gracefully.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through memoizing the users fetch so repeat renders don't re-request, maybe storing it in context or a lightweight cache depending on app size.
Start by clarifying the caching strategy for the users list, such as using a client-side cache with a library like React Query or SWR, and discuss cache invalidation and staleness. Then, for both API calls, outline a consistent approach to loading and error states, including UI feedback, retry mechanisms, and graceful degradation. Emphasize trade-offs between simplicity and robustness, and how you would handle edge cases like partial failures.
Pro tip: Mention that you would implement optimistic updates for mutations to the users list to improve perceived performance, and use error boundaries to isolate failures. Also, highlight the importance of logging and monitoring to track cache hit rates and error frequencies.
Ask about the expected frequency of updates, data size, and whether the users list is shared across components. This determines if a simple in-memory cache or a more sophisticated solution like IndexedDB is needed.
Propose using a library like React Query or SWR for declarative caching, with stale-while-revalidate and automatic retries. Discuss cache invalidation strategies (e.g., time-based or manual) and how to handle mutations.
For both API calls, use skeleton loaders or spinners to indicate loading, and consider showing cached data while revalidating in the background. Ensure loading states are accessible and don't cause layout shifts.
Implement error boundaries or error UI components that display user-friendly messages and retry options. For the users list, consider showing stale data with an error banner if the refresh fails.
Compare custom caching vs. library solutions, and address scenarios like race conditions, offline support, and cache consistency across tabs. Mention how you would test these states.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.