Start by clarifying functional and non-functional requirements, then sketch a high-level architecture that separates concerns: client apps, API gateway, services for feed personalization, article reading, and read tracking. Dive into data modeling and API design for the core features, and discuss scalability strategies like caching, sharding, and CDNs to handle a very large user base.
Pro tip: Emphasize the read/unread tracking as a write-heavy, eventually consistent problem—propose a scalable solution like a dedicated service with a time-series or wide-column store, and discuss trade-offs between consistency and latency. Also, mention how personalization can be decoupled and served via precomputed feeds to reduce read latency.
Ask questions to understand expected user scale (e.g., millions of DAU), read/write patterns, latency requirements, and whether personalization is real-time or batch. Define core features: personalized feed, article reading, and read tracking.
Outline components: mobile/web clients, API gateway, services for feed, article, and read-tracking, plus data stores (SQL/NoSQL, cache, CDN). Explain how they interact and where to apply scaling techniques like load balancing and horizontal scaling.
Design schemas for users, articles, feeds, and read status. Choose appropriate databases: e.g., Cassandra for read tracking (write-heavy, time-series), Redis for caching feeds, and a relational DB for articles. Discuss sharding and replication.
Define RESTful or GraphQL endpoints for fetching feed, reading an article, and marking as read. Consider pagination, rate limiting, and authentication. Discuss how mobile and web clients consume these APIs efficiently.
Address bottlenecks: feed generation (precompute vs. on-the-fly), read tracking (eventual consistency vs. strong), and caching strategies. Discuss trade-offs between consistency, availability, and latency, and how to handle failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I talked through CDN caching for semi-personalized feeds and pre-computation for ranking.
Start by clarifying requirements and constraints, then propose a high-level architecture that separates online serving from offline/batch processing. Focus on precomputation, caching, and efficient data access to meet the 200ms P95 latency at 1M RPS. Discuss trade-offs and potential bottlenecks.
Pro tip: Emphasize that at this scale, you cannot compute personalized rankings on the fly; precomputation and aggressive caching are essential. Also, mention the importance of monitoring and fallback strategies to handle failures gracefully.
Ask questions to understand the scope: What defines personalization? How fresh must the feed be? What are the read/write patterns? Confirm latency and throughput targets.
Propose a system that precomputes personalized feeds offline (e.g., via batch jobs or stream processing) and stores them in a low-latency data store. Use a CDN or edge caching for popular content.
Choose a scalable, low-latency storage solution (e.g., Redis, DynamoDB) to store precomputed feeds. Design the API to fetch the feed with a single key lookup, minimizing network hops.
Address how to handle 1M RPS: shard the data store, use read replicas, implement caching layers, and consider asynchronous updates. Discuss load balancing and auto-scaling.
Discuss trade-offs between freshness and latency, cost implications, and fallback mechanisms (e.g., return a non-personalized feed if personalization fails). Mention monitoring and alerting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements (scale, consistency needs, offline duration) and then propose a client-server architecture with a local store (e.g., IndexedDB/SQLite) and a backend service. Use an append-only event log or versioned records with last-write-wins or CRDTs to sync changes, and handle offline via a durable outbox queue with retries and conflict resolution.
Pro tip: Mention that read state is often a monotonic 'read up to' timestamp or version, so you can use a simple max() merge instead of complex CRDTs, and always make sync idempotent to avoid duplicates on retry.
Ask about scale (users, devices), consistency model (strong vs eventual), offline duration, and whether read state is per-item or a high-water mark. This shapes the data model and sync strategy.
Model read state as a versioned record (e.g., {userId, itemId, readAt, version}) or a monotonic high-water mark per user. Use a client-generated UUID or version to make updates idempotent.
Use a pull-based sync with delta updates (e.g., last sync timestamp) and push changes via an outbox queue. For conflicts, apply last-write-wins or max() for monotonic values, or use CRDTs if needed.
Store pending changes in a durable local queue (e.g., IndexedDB) and retry with exponential backoff. On reconnect, sync in order and reconcile conflicts using the chosen strategy.
Discuss multi-device concurrency, clock skew, storage limits, and performance. Explain trade-offs between simplicity (LWW) and correctness (CRDTs) and how you'd monitor sync health.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went client cache for read state and recently viewed articles, CDN for article bodies since they're mostly static, server-side cache for ranked feeds with user-segment-level granularity rather than pure per-user to keep cache hit rates reasonable.
Start by contrasting the caching needs of the feed (highly dynamic, personalized, real-time) versus article content (mostly static, cacheable, read-heavy). Then walk through each layer—client, CDN, and server—explaining what you cache, how you invalidate, and the trade-offs you make for consistency, latency, and cost.
Pro tip: Emphasize cache invalidation strategies and how you handle personalized content at the edge without sacrificing cache hit rates. Mention that you'd measure cache hit ratio and adjust TTLs based on data, showing a metrics-driven approach.
Ask about feed update frequency, personalization level, article update cadence, and consistency requirements. This shows you tailor caching to business needs rather than applying a one-size-fits-all solution.
For articles, use HTTP caching headers (Cache-Control, ETag) and local storage for offline reading. For feeds, use in-memory caching with short TTLs and background refresh to balance freshness and performance.
Cache articles at the edge with long TTLs and stale-while-revalidate. For feeds, use edge caching with short TTLs and vary by user segment or use surrogate keys for targeted purging.
Use Redis or Memcached to cache rendered articles and feed fragments. For feeds, cache per-user or per-segment with short TTLs and invalidate on new content or user actions.
Describe strategies like TTL-based expiry, event-driven purging (e.g., on article update), and versioned keys. Discuss trade-offs between consistency and cache hit rate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scale and latency requirements, then propose a layered caching strategy with short TTLs and proactive invalidation for hot content. Emphasize trade-offs between consistency, latency, and cost, and suggest monitoring and adaptive mechanisms to handle trending spikes.
Pro tip: Mention that for hot content, you might use a 'stale-while-revalidate' pattern to serve slightly stale data while refreshing in the background, balancing freshness and load. Also, consider using a write-through cache with a message queue for invalidation to avoid thundering herds.
Ask about read/write ratios, acceptable staleness, latency SLAs, and traffic patterns to understand the problem scope.
Propose multiple cache layers (CDN, application cache, database cache) with appropriate TTLs and eviction policies for hot content.
Describe event-driven invalidation (e.g., pub/sub, message queues) and proactive refresh for trending items to minimize stale reads.
Discuss techniques like request coalescing, rate limiting, and serving stale data during spikes to protect backend systems.
Outline metrics (hit rate, latency, staleness) and adaptive TTL adjustments based on content popularity to continuously optimize.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.