Jumped straight into queuing and async writes, which felt right, but I spent too long on the ingestion layer and barely touched replication strategy before time ran out.
Start by clarifying requirements: expected write volume (e.g., writes per second), data size, latency tolerance, consistency needs, and durability guarantees. Then propose a scalable architecture that distributes writes across partitions, uses asynchronous processing, and optimizes storage for high write throughput, while discussing trade-offs like consistency vs. availability and cost.
Pro tip: Emphasize that high write throughput often requires decoupling ingestion from processing (e.g., via a log or queue) and that eventual consistency is usually acceptable for write-heavy systems. Also, mention the importance of monitoring and backpressure to handle bursts.
Ask about write volume, data size, latency, consistency, durability, and budget. This ensures the design meets actual needs and shows you avoid assumptions.
Propose a distributed system with a write-optimized ingestion layer (e.g., API gateway, load balancer), a durable message queue or log (e.g., Kafka), and a partitioned storage layer (e.g., Cassandra, DynamoDB).
Explain how data is partitioned (e.g., by key hash) to distribute writes evenly, and how to scale horizontally by adding nodes. Discuss replication for durability and availability.
Detail techniques like batching, asynchronous writes, append-only logs, LSM trees, and in-memory buffers to maximize throughput. Mention trade-offs like increased latency or complexity.
Discuss consistency models (e.g., eventual consistency), durability guarantees, backpressure, and how to handle node failures, hot partitions, and data skew.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.