I started with the data model because I needed something to anchor the whole thing.
Start by clarifying requirements (scale, durability guarantees, snapshot frequency) and then present a layered design: a block store with copy-on-write and a metadata tree for files/directories. Walk through the write path with journaling/WAL for crash consistency, then explain snapshot/restore via immutable root pointers, and finish with bottlenecks and production scaling considerations.
Pro tip: Emphasize the trade-off between durability and performance: e.g., fsync on every write vs. group commit, and how Netflix's scale might favor eventual consistency for some operations. Also, mention that snapshots are cheap with copy-on-write but can cause fragmentation, so garbage collection is key.
Ask about expected scale (files, size, ops/sec), durability guarantees (e.g., can we lose recent writes?), snapshot frequency, and whether the system is in-memory or disk-backed. This shapes the design.
Propose a hierarchical namespace (inodes/dentries) with copy-on-write blocks. Describe create, read, write, delete: writes allocate new blocks, update metadata, and atomically switch pointers.
Use a write-ahead log (WAL) or journal for metadata and data, with fsync for critical updates. On crash, replay the log to recover to a consistent state, then apply snapshots.
Snapshots are immutable root pointers to the metadata tree; restore is just switching back to an old root. Discuss space reclamation via reference counting or garbage collection.
Identify bottlenecks: metadata contention, fsync latency, snapshot overhead. For production, discuss sharding, distributed storage, caching, and asynchronous replication.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.