I started with the API surface which felt safe, but then they pushed immediately into partitioning and I jumped to consistent hashing before fully thinking through the replication story.
Start by clarifying requirements (scale, consistency, latency, availability) and then propose a sharded, replicated architecture using consistent hashing and a consensus protocol like Raft for strong consistency. Discuss trade-offs between consistency models (e.g., eventual vs. strong) and how to achieve low p99 latency through techniques like caching, batching, and efficient data structures.
Pro tip: Emphasize that TTL and compare-and-swap require careful handling in a distributed system—use logical clocks or versioning to avoid race conditions, and consider lazy expiration with background cleanup to keep latency low.
Ask about expected read/write ratio, consistency requirements, latency targets, and data size to tailor the design. Confirm that TTL and CAS are optional and can be configured per key.
Propose a sharded, replicated system using consistent hashing for partitioning and a replication factor for fault tolerance. Use a coordinator node or client-side routing to direct requests to the appropriate shard.
Design the storage layer: use LSM-trees (e.g., RocksDB) for high write throughput and efficient range scans. Store keys with metadata (version, TTL) and values as byte strings. Implement TTL via expiration timestamps and background compaction.
Choose a consistency model: for strong consistency, use Raft or Paxos per shard; for eventual consistency, use gossip or anti-entropy. Implement CAS using version numbers or conditional writes with quorum reads/writes.
Discuss techniques to achieve low p99 latency: caching hot keys, batching requests, asynchronous replication, and load balancing. Ensure high availability with automatic failover and multi-datacenter replication.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.