Spent the first few minutes just trying to scope it out, which I think was the right call, but I probably over-indexed on the producer side and barely touched consumer group semantics until they nudged me.
Start by clarifying requirements (scale, durability, ordering, latency) and then design a high-level architecture with core components: producers, brokers, topics/partitions, consumers, and storage. Dive into key design decisions like partitioning, replication, delivery semantics, and fault tolerance, discussing trade-offs at each step. Conclude by addressing scalability, monitoring, and operational concerns.
Pro tip: Emphasize trade-offs explicitly—interviewers want to see that you understand why you chose a particular approach over alternatives, not just what you chose. Relate decisions to Amazon's leadership principles like 'Customer Obsession' and 'Ownership' by tying them to real-world impact.
Ask questions to understand expected scale (messages/sec, data volume), durability guarantees, ordering requirements, latency targets, and consumer patterns. This ensures the design meets actual needs.
Sketch the main components: producers, brokers, topics/partitions, consumers, and storage. Explain how messages flow from producers to consumers via brokers.
Detail partitioning strategy for scalability, replication for fault tolerance, and storage design for durability and performance. Discuss message retention and delivery semantics (at-least-once, at-most-once, exactly-once).
Explain how the system scales horizontally (adding brokers, partitions) and handles failures (replication, leader election, consumer rebalancing). Discuss monitoring and alerting.
Compare design choices (e.g., push vs. pull, Kafka vs. RabbitMQ) and justify decisions based on requirements. Mention potential bottlenecks and mitigation strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.