This was the bulk of the first half and I actually felt decent about it.
Start by clarifying requirements and scale, then propose a client-first architecture with a local cart model that syncs to the backend. Address each sub-topic (anonymous vs. logged-in, multi-tab, offline, real-time updates, data modeling, state management, sync, performance, observability) with concrete trade-offs and a clear recommendation.
Pro tip: Emphasize idempotency and conflict resolution in sync—use a versioned cart with operation-based merging to handle multi-tab and offline edits gracefully. Also, highlight how you'd measure success with metrics like cart abandonment rate and sync latency.
Ask about expected traffic, user base, and business constraints (e.g., guest checkout, promotions). Define non-functional requirements like latency, consistency, and offline duration.
Propose a cart data model (e.g., items with SKU, quantity, price snapshot) and client-side state management (e.g., Redux, MobX, or local storage) that supports offline and multi-tab.
Outline a sync strategy with the backend: optimistic updates, versioning, and conflict resolution (e.g., last-write-wins or operational transforms). Handle anonymous-to-logged-in cart merging.
Explain how to handle price/stock changes via WebSockets or polling, and how to queue offline actions and reconcile when back online.
Discuss performance optimizations (e.g., debouncing, caching) and observability (logging, metrics, tracing) to monitor cart operations and sync health.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and scale, then propose a high-level architecture with a central flag service, client SDKs, and a CDN-backed delivery layer. Walk through the key components: targeting rules, percentage rollout, consistency mechanisms, safety defaults, and A/B testing hooks, while discussing trade-offs at each step.
Pro tip: Emphasize that flag evaluation should be deterministic and client-side using a consistent hashing algorithm with a stable identifier (e.g., user ID or device ID) to ensure consistent experiences across sessions and devices. Also, mention the importance of a kill switch and default values to prevent outages.
Ask about expected QPS, number of flags, user base size, latency requirements, and whether flags need to be evaluated client-side or server-side. This shapes the architecture and trade-offs.
Outline a flag management service (UI + API), a rules engine for targeting and percentage rollout, a delivery mechanism (CDN, SDKs), and a data store for flag configurations and audit logs.
Describe how to achieve consistent evaluation across sessions/devices using deterministic hashing and stable identifiers. Discuss safety defaults, kill switches, and fallback mechanisms to handle service failures.
Explain how to expose flag variations to analytics systems, log exposures, and integrate with experimentation platforms. Mention the need for statistical rigor and avoiding common pitfalls like sample ratio mismatch.
Compare push vs. pull delivery, client-side vs. server-side evaluation, and caching strategies. Address scalability, latency, and how the system can evolve with changing needs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.