Design an Ad Click Aggregator — system design interview guide

Design an Ad Click Aggregator is a depth lesson in the Search, Geo & Aggregation family of the Thita system design course, about 60 minutes long, and reported asked at Meta, Google.

Full-text search, geospatial indexing, stream processing, and probabilistic structures.

It sits on the Deep Dive path — worth reading once the Interview Soon lessons are solid, or when a role names this area explicitly.

The rest of Search, Geo & Aggregation

• Search & Geospatial — Important lesson.
• Real-Time Aggregation — Important lesson.
• Elasticsearch Deep Dive — Depth lesson.
• Probabilistic Structures Deep Dive — Depth lesson.
• Design Uber — Core lesson, worked case study, about 60 minutes, asked at Uber, Lyft.
• Design an Ad Click Aggregator — Depth lesson, worked case study, about 60 minutes, asked at Meta, Google.
• Design Uber Deep Dive — Depth lesson, worked case study, about 60 minutes, asked at Uber.
• Design a Top-K Leaderboard — Depth lesson, worked case study, about 60 minutes, asked at Amazon, Meta, Riot Games.
• Design Google Maps — Depth lesson, worked case study, about 60 minutes, asked at Google, Uber, Apple.

Other system design pattern families

• Foundations — The 45-minute interview framework, estimation with modern numbers, and CAP theorem. 6 lessons · about 2 hours · Interview Soon · 1 worked case study.
• Networking & APIs — Protocols (REST, gRPC, WebSocket, SSE), API design, pagination, and idempotency. 5 lessons · about 2 hours · Interview Soon · 1 worked case study.
• Databases — SQL vs NoSQL decision tree, replication, sharding, and indexing strategies. 7 lessons · about 3 hours · Interview Soon · 2 worked case studies.
• Caching — Cache-aside, write-through, failure modes, and when NOT to cache. 5 lessons · about 1.5 hours · Interview Soon · 2 worked case studies.
• Queues & Async — Kafka vs SQS, delivery guarantees, idempotent consumers, and event-driven patterns. 10 lessons · about 1.5 hours · Interview Soon · 5 worked case studies.
• Scalability — Consistent hashing, read vs write scaling, fan-out strategies, and circuit breakers. 7 lessons · about 1.5 hours · Interview Soon · 4 worked case studies.
• Coordination & Transactions — Raft consensus, distributed locking, saga pattern, and conflict resolution. 9 lessons · about 1.5 hours · Interview Soon · 5 worked case studies.
• Reliability & Operations — Monitoring, observability, deployment strategies, and graceful degradation. 2 lessons · about 3 hours · Deep Dive.
• Security & Auth — OAuth 2.0, JWT vs sessions, API security, and encryption. 2 lessons · about 3 hours · Deep Dive.
• AI Infrastructure — LLM serving, inference batching, RAG pipelines, and vector search at scale. 6 lessons · about 6 hours · Deep Dive · 1 worked case study.
• Real-World Architecture Studies — Deep dives into Discord, Uber, Stripe, Netflix, and Kafka production architectures. 5 lessons · about 4 hours · Deep Dive.

Related interview preparation

• Looking for the practice sheet? — Track your progress against every family as a checklist, with company tags and AI interview practice.
• Low Level Design course — The object-level companion: OOP, SOLID, the design patterns and the machine-coding round.
• DSA patterns course — The coding round: the algorithmic patterns behind the problems asked alongside system design.
• Latency and capacity numbers — The reference table of modern latency, throughput and storage figures used in estimation.
• AI mock interviews — Practise the spoken answer against an interviewer that pushes back on the trade-offs.