This question sprawled way more than I expected.
Start by clarifying requirements (e.g., consistency, latency, scale) and then propose a high-level architecture that separates read and write paths. Use an in-memory store like Redis for real-time counts, with a write-behind cache to a durable database, and consider sharding by item ID to handle the load. Discuss trade-offs between consistency and availability, and how to handle hot items.
Pro tip: Mention that you would use a combination of local caching and a distributed cache like Redis with read replicas to handle 1M QPS reads, and for writes, use a message queue to batch updates to the database to avoid overwhelming it.
Ask about consistency (strong vs eventual), latency targets, and whether counts need to be exact or approximate. Also confirm the scale: 1M reads and 100K writes per second.
Propose a microservices architecture with separate services for reads and writes. Use a distributed cache (e.g., Redis) for real-time counts, and a durable database (e.g., Cassandra) for persistence.
Design the data model: each item has a favorite count. Use Redis hashes or counters, sharded by item ID. For persistence, use a wide-column store with item ID as partition key and count as a column.
For reads: use read replicas and local caching (e.g., CDN or in-memory). For writes: use a message queue (e.g., Kafka) to buffer writes and update Redis asynchronously, then persist to DB in batches.
Discuss consistency vs performance: eventual consistency may be acceptable. Handle hot items by sharding counters or using a distributed counter. Consider idempotency for writes and failure recovery.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.