Start by clarifying requirements: define 'too many times' (e.g., max 3 impressions per user per ad per day) and the rolling window (e.g., 24 hours). Then propose a scalable, low-latency solution using a distributed in-memory store like Redis with per-user per-ad counters and TTLs, and discuss trade-offs between accuracy, latency, and cost.
Pro tip: Mention that frequency capping must be enforced at ad-serving time with minimal latency (<10ms), so precomputing or caching counts is essential; also highlight the need to handle clock skew and eventual consistency in distributed systems.
Ask about the definition of frequency cap (e.g., max impressions per user per ad per time window), the size of the rolling window, and whether it's per ad, per campaign, or per advertiser. Also clarify scale (users, ads, QPS) and latency requirements.
Propose a distributed key-value store (e.g., Redis) to track impression counts per user per ad with TTL equal to the rolling window. For each ad request, check the count; if below cap, serve ad and increment count atomically.
Design keys like 'freq:{userId}:{adId}' storing a counter, or use a sorted set of timestamps for precise rolling windows. Discuss memory footprint and eviction policies.
Address sharding by user ID, replication for high availability, and trade-offs between strong consistency (using atomic increments) and eventual consistency (using local caches with periodic sync). Consider using a write-through cache or a dedicated frequency capping service.
Discuss trade-offs: exact counting vs. approximate (e.g., using probabilistic data structures like count-min sketch), latency vs. accuracy, and cost. Handle edge cases like clock skew, TTL expiration, and failure scenarios (e.g., Redis down).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.