I started with the data model and spent probably too long on it.
Start by clarifying requirements and scale, then design a data model that efficiently tracks likes per post and per user, and finally propose a scalable architecture for near-real-time updates. Focus on trade-offs between consistency, latency, and cost, and explain how you would handle high write throughput and read fan-out.
Pro tip: Discuss how to handle idempotency and race conditions (e.g., double-clicks or concurrent like/unlike) using unique constraints or atomic operations, and mention the importance of eventual consistency for like counts to achieve low latency at scale.
Ask about expected read/write QPS, latency requirements for near-real-time, consistency needs, and whether the system must handle celebrity posts with millions of likes. Also clarify if users can like multiple times or if it's a toggle.
Propose a schema that stores likes per user-post pair (e.g., a likes table with user_id, post_id, timestamp) and a separate counter per post for fast reads. Consider using a wide-column store or a relational DB with proper indexing.
Outline how likes are written (e.g., via API that updates the likes table and increments a counter asynchronously) and how reads are served (e.g., from a cache or denormalized counter). Discuss using a message queue or change data capture to update counters.
Explain how to push updates to clients (e.g., WebSockets, SSE, or polling) and how to aggregate counts in near-real-time. Mention using a pub/sub system to broadcast like events to interested clients.
Discuss trade-offs: strong vs eventual consistency for counts, cost of maintaining per-user like status, and strategies for hot posts (e.g., sharding counters, using CRDTs). Also address idempotency and race conditions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: the source-of-truth likes database must remain authoritative, while the aggregated counts store needs to be eventually consistent and read-optimized. Then propose a CDC pipeline that captures changes from the source database and applies them to the aggregated store, discussing trade-offs around consistency, latency, and failure handling.
Pro tip: Emphasize idempotency and exactly-once processing to avoid double-counting likes, and mention how you would handle schema evolution and backfills without disrupting the live system.
Ask about consistency needs (e.g., eventual vs. strong), expected throughput, latency tolerance, and whether the aggregated store can tolerate temporary inconsistencies.
Select a CDC approach such as log-based (e.g., Debezium, MySQL binlog) or trigger-based, and justify why log-based is preferable for low overhead and real-time capture.
Outline how changes flow from the source to the aggregated store, including message queue (e.g., Kafka), stream processing (e.g., Flink, Kafka Streams), and idempotent updates to the counts store.
Discuss how to handle duplicates, out-of-order events, and failures (e.g., using idempotent writes, deduplication, and dead-letter queues) to ensure eventual consistency.
Mention monitoring, backfill strategies, schema evolution, and how to bootstrap the aggregated store from the source database initially.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through a write-through cache with a short TTL and mentioned that hot posts need special treatment.
Start by clarifying the scale and consistency requirements, then propose a multi-layer caching strategy (client, CDN, application cache, database) with write-through or write-behind updates. Emphasize handling hot keys via sharding, local caching, and probabilistic early expiration to avoid stampedes.
Pro tip: Mention that like counts are often eventually consistent and can be approximated with a counter service like Redis, but you must handle cache invalidation and thundering herd with techniques like request coalescing or jittered TTLs.
Ask about read/write ratio, consistency needs (strong vs eventual), and scale (e.g., millions of likes per second). This shows you don't jump to solutions.
Propose client-side caching, CDN for static assets, and an in-memory cache (Redis/Memcached) for like counts. Explain how each layer reduces load.
Describe techniques like sharding the counter across multiple keys, using local caches on app servers, and request coalescing to prevent cache stampedes.
Discuss write strategies: write-through, write-behind, or periodic batch updates to the database. Mention trade-offs between latency and durability.
Explain how to monitor cache hit rates, latency, and error rates, and dynamically adjust TTLs or scale cache nodes during spikes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the trickiest part of the whole question.
Start by clarifying the requirements and scale, then propose a design that ensures idempotency and atomicity for like/unlike operations. Discuss how to handle race conditions using techniques like optimistic locking, distributed locks, or event sourcing, and explain how to prevent double-counting with unique constraints or idempotent processing.
Pro tip: Mention that you would use a combination of client-side debouncing and server-side idempotency keys to handle rapid toggles gracefully, and emphasize the importance of monitoring and alerting for anomalies in like counts.
Ask about expected traffic, consistency requirements, and whether eventual consistency is acceptable. Understand the impact of double-counting on user experience and business metrics.
Ensure each like/unlike action is idempotent by using unique identifiers (e.g., user ID + post ID) and idempotency keys. Store actions in a way that duplicate requests don't change the state.
Use atomic operations (e.g., database transactions, Redis Lua scripts) or optimistic concurrency control (version numbers) to serialize updates. Consider distributed locks if needed, but be mindful of performance.
Maintain a separate likes table with a unique constraint on (user_id, post_id) to ensure each user can like a post only once. Use upserts or conditional writes to toggle the like status.
Compare approaches: strong consistency vs. eventual consistency, database vs. cache, and synchronous vs. asynchronous processing. Explain how your choice scales and handles failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.