Start by clarifying requirements and scale (e.g., QPS, latency, accuracy), then design a pipeline: ingestion with idempotency, deduplication/fraud detection, real-time and batch aggregation, storage tiers for raw and rolled-up data, and query layers for billing/reporting. Emphasize trade-offs (latency vs accuracy, cost vs performance) and how you'd ensure scalability and consistency at each stage.
Pro tip: Proactively discuss how you'd handle late-arriving clicks and exactly-once semantics in billing, as these are common pitfalls that demonstrate deep understanding of real-world ad systems.
Ask about expected QPS, click volume, latency requirements, accuracy needs (e.g., billing vs analytics), and data retention. This sets the stage for design decisions.
Outline a scalable ingestion endpoint (e.g., HTTP/2, load balancers, async processing) with idempotency keys and deduplication using a fast store (e.g., Redis, Bloom filters). Include fraud detection via rules or ML models.
Describe a lambda architecture: real-time stream processing (e.g., Flink, Kafka Streams) for immediate metrics, and batch processing (e.g., Spark) for accurate, cost-effective rollups. Discuss trade-offs.
Store raw clicks in a scalable data lake (e.g., S3, HDFS) and rolled-up aggregates in a time-series or OLAP database (e.g., Druid, ClickHouse) with different granularities (minute, hour, day).
Design query APIs for billing (exact, consistent) and reporting (eventual consistency, fast). Discuss sharding, replication, partitioning, and how to handle late data and exactly-once semantics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.