This is basically 'design Kafka from scratch' and they know it.
Start by clarifying requirements and scale, then outline the high-level architecture with components like brokers, partitions, and replicas. Dive into the write path (append-only segments, indexing) and read path (sequential reads, caching), then discuss replication, leader election, recovery, retention, and compaction. Emphasize trade-offs and how your design meets Google-scale reliability and performance.
Pro tip: Anchor your design around the log abstraction (like Kafka's commit log) and explicitly discuss how you'd handle failure scenarios and data durability, as Google values reliability and operational excellence.
Ask about expected throughput, latency, data size, retention needs, and consistency requirements. Establish assumptions for scale (e.g., petabytes, millions of writes/sec).
Sketch components: producers, brokers, partitions, replicas, metadata service, and consumers. Explain partitioning strategy and how it enables scalability.
Detail append-only writes to segments, indexing (offset index, time index), and sequential reads. Discuss caching, zero-copy, and batching for high throughput.
Describe replication protocol (e.g., ISR), leader election, follower catch-up, and recovery from failures. Mention trade-offs between consistency and availability.
Explain retention policies (time/size-based), segment rolling, compaction (log cleaning), and monitoring. Discuss how these impact performance and storage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.