I had actually prepped this one pretty thoroughly and felt decent walking through the flow.
Start by clarifying requirements: event volume, latency, query patterns, and data retention. Then propose a scalable ingestion pipeline (e.g., Kafka) with stream processing for aggregation, and a storage layer optimized for analytical queries (e.g., columnar store). Finally, discuss trade-offs and how to handle scale, failures, and exactly-once semantics.
Pro tip: Emphasize partitioning and windowing strategies early, as they are critical for scalability and correctness. Also, mention how you would handle late-arriving events and ensure idempotency, showing awareness of real-world complexities.
Ask about event volume (e.g., millions per second), required latency for aggregation (real-time vs batch), query patterns (e.g., ad performance metrics), and data retention. This scopes the design.
Propose a pipeline: ingestion (e.g., Kafka) -> stream processing (e.g., Flink, Spark Streaming) -> storage (e.g., Druid, ClickHouse) -> query layer. Highlight scalability and fault tolerance.
Define event schema and aggregation keys (e.g., ad_id, timestamp). Discuss windowing (tumbling, sliding) and how to handle late data (watermarks, allowed lateness).
Explain partitioning (by ad_id or time), replication, and backpressure. Address exactly-once semantics using idempotent writes or transactional sinks.
Compare batch vs stream processing, discuss cost vs latency, and suggest optimizations like pre-aggregation, indexing, and tiered storage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.