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Namespace vs bytes, erasure coding vs replication, the eleven-nines durability math, multipart upload, and why LIST is the hardest API in the system.
Design a planet-scale object store: clients PUT an immutable blob under a key inside a bucket, GET it back by key, DELETE it, and LIST keys by prefix. Objects range from bytes to terabytes; the system stores trillions of them with eleven nines of durability and serves millions of requests per second.
This is the storage-infrastructure interview at its purest. The API surface is four verbs; everything interesting is beneath it: how the namespace (keys, metadata) is separated from the bytes, how durability is engineered rather than hoped for, how uploads of multi-GB objects survive flaky networks, and what consistency guarantees the metadata layer can honestly make. Strong candidates do the durability math explicitly and treat LIST - not GET - as the API that shapes the metadata design.
Asking these before diving into a solution is the difference between a "hire" and a "no signal" rating. Pick the questions whose answers would change your design.
Capacity estimation · architecture with all 7 components explained · 6 deep dives · trade-off analysis · 8 common follow-up questions
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Consistent hashing, eviction, replication, and what really happens when a single hot key takes down the cluster.
Edge cache hierarchies, cache key design, invalidation, origin shield, and edge compute - the system every other system relies on without thinking about it.
Encoding ladders, adaptive bitrate, CDN economics, and the difference between live and VOD. Petabyte-scale storage meets millisecond-scale playback.
Raft leader election, log replication, snapshots - and the CAP theorem in operational practice. The substrate every other distributed system stands on.
Reading is the floor. The interview signal is in walking through this live with someone probing follow-ups. Use the AI mock interview to practice talking through requirements, architecture, and trade-offs out loud.
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