This is the kind of question where you can go in a dozen directions and that's kind of the problem.
Start by clarifying requirements: expected write throughput, read patterns, latency, consistency, and durability needs. Then propose a high-level architecture that decouples writes from reads using a log-based ingestion layer (e.g., Kafka) and a horizontally partitioned storage engine (e.g., LSM-tree based). Finally, dive into trade-offs around partitioning, replication, consistency, and failure handling.
Pro tip: Quantify the scale early (e.g., 'Assume 1M writes/sec') to ground design decisions and show you can reason about capacity. Also, explicitly discuss how you'd handle hotspots and backpressure, as these are common failure modes in write-heavy systems.
Ask questions to understand write volume, read/write ratio, latency SLAs, consistency requirements, and data retention. Estimate peak throughput and data size to inform design choices.
Propose a layered design: ingestion layer (e.g., Kafka) for buffering and decoupling, storage layer (e.g., distributed LSM-tree like Cassandra or HBase) for high write throughput, and optionally a serving layer for reads.
Explain how data is partitioned (e.g., consistent hashing) to distribute writes evenly and replicated (e.g., quorum-based) for durability and availability. Discuss trade-offs between replication factor, consistency, and latency.
Detail how writes are handled: append-only logs, in-memory buffers (memtables), and periodic flushing to disk (SSTables). Mention techniques like write-ahead logging, batching, and compression to improve throughput.
Discuss how the system handles node failures, hotspots, and scaling. Cover mechanisms like hinted handoff, anti-entropy repair, and dynamic partitioning. Also address backpressure and load shedding.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.