← Snowflake Interview Insights
Start by clarifying requirements: scale, latency, consistency needs, and workload patterns. Then design a distributed key-value store with a storage engine (e.g., LSM-tree), a transaction manager for ACID (using MVCC and 2PL or optimistic concurrency), and a replication protocol (e.g., Raft) for fault tolerance. Discuss trade-offs between consistency, availability, and performance.
Pro tip: At Snowflake, emphasize how your design handles concurrent access without sacrificing ACID, and discuss how you would leverage cloud storage for durability and scalability. Show awareness of real-world constraints like network partitions and clock skew.
Ask about expected scale (data size, QPS), latency SLAs, consistency requirements (strong vs. eventual), and workload characteristics (read-heavy vs. write-heavy). This shapes the design.
Propose a distributed architecture with sharding/partitioning, replication for fault tolerance, and a client-facing API. Mention components like a coordinator, storage nodes, and a metadata service.
Choose a storage engine (e.g., LSM-tree for write-heavy, B-tree for read-heavy) and describe how data is stored on disk. Explain how keys are mapped to partitions.
Detail how you achieve atomicity, consistency, isolation, and durability. Discuss concurrency control mechanisms like MVCC, 2PL, or optimistic concurrency, and how they handle conflicts.
Discuss trade-offs (e.g., consistency vs. latency, isolation levels) and how the system handles failures (node crashes, network partitions). Mention replication protocols like Raft or Paxos.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.