Spent the first few minutes just trying to scope it, which I think was the right call but I probably talked too long before drawing anything.
Start by clarifying requirements and constraints (scale, latency, durability, query needs) before diving into architecture. Then propose a high-level design with clear components (ingestion, storage, indexing, query, visualization) and discuss trade-offs for each. Finally, dive deep into one or two critical areas like partitioning or fault tolerance, and wrap up with monitoring and operational considerations.
Pro tip: Amazon values customer obsession and operational excellence, so frame your design around the needs of the log producers and consumers (e.g., developers debugging issues) and emphasize how you'd handle failures, scaling, and cost at Amazon's scale.
Ask questions to understand the scale (logs per second, data volume), latency requirements (real-time vs batch), durability guarantees, query patterns, and retention policies. This ensures your design meets actual needs and shows you think before coding.
Sketch the main components: log producers (agents), ingestion pipeline (e.g., Kafka), storage (e.g., distributed file system or NoSQL), indexing (e.g., Elasticsearch), and query/visualization layer. Explain how data flows from producers to consumers.
Pick 1-2 areas to detail, such as partitioning strategy for scalability, replication for durability, or indexing for fast search. Discuss trade-offs (e.g., consistency vs availability, cost vs performance).
Explain how the system scales horizontally, handles failures (e.g., node crashes, network partitions), and ensures data durability. Include monitoring, alerting, and cost optimization strategies.
Recap the design, highlighting key decisions and their trade-offs. Mention potential improvements or alternative approaches, showing awareness of evolving requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.