I started with the read path because that felt most urgent, deciding whether to show an ad, and worked backward from there.
Start by clarifying requirements: define the ad scopes (e.g., campaign, creative), the rolling window (e.g., 24 hours), and the cap N. Then propose a scalable, low-latency design using a distributed store like Redis with sorted sets or counters, and discuss trade-offs between accuracy, latency, and cost. Finally, address edge cases like clock skew, hot keys, and failure modes.
Pro tip: Emphasize the need for a rolling window rather than fixed windows to avoid boundary effects, and mention using approximate counting (e.g., Redis sorted sets with TTL) to balance precision and performance at Netflix scale.
Ask questions to pin down ad scopes (e.g., per campaign, per creative), the rolling window duration, the cap value N, and expected QPS. Also clarify consistency requirements (strict vs eventual) and latency SLOs.
Choose a data model: for each user and ad scope, store a timestamped list of impressions. Use a distributed store like Redis with sorted sets (ZADD) and TTL to automatically expire old entries. Discuss sharding by user ID to distribute load.
On each ad request, check the count of impressions within the rolling window. If count < N, allow and record the impression; else, deny. Use atomic operations (e.g., Lua scripts in Redis) to avoid race conditions.
Discuss scaling: shard by user, use read replicas, and consider approximate counting (e.g., HyperLogLog) if exact counts are too costly. Handle failures with fallbacks (e.g., allow if store is down) and monitor hot keys.
Compare exact vs approximate counting, in-memory vs persistent storage, and synchronous vs asynchronous updates. Mention optimizations like local caching, batching, and pre-aggregation for high-traffic users.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.