This is the design that every other distributed-systems idea plugs into. Build a key-value store that never says no to a write — always available, horizontally scalable, no single coordinator — and you end up re-deriving Amazon’s Dynamo, the paper that shaped Cassandra, Riak, and DynamoDB.
Two operations, four hard problems
The API is trivial; the interview is the four problems hiding behind it:
- Where does a key live? (placement)
- How many copies, and where? (replication)
- How fresh must a read be? (consistency)
- What happens when a node is down? (failure)
- Consistent hashing — a ring of nodes
- N replicas on the next N nodes clockwise
- Quorums: R + W > N tunes consistency
- Hinted handoff + Merkle-tree repair
The one inequality at the heart of it
Each key is stored on N nodes. A write waits for W acknowledgements; a read gathers R responses. That’s it — and the relationship between those three numbers is the entire consistency story:
R + W > N guarantees the read and write sets overlap on at least one node — the pigeonhole principle doing consistency’s heavy lifting. Set R=W=1 and reads are lightning fast but may be stale; set W=N and writes are durable but block on your slowest replica.
The members-only build assembles the whole store: an interactive quorum explorer where you drag N, R, W and watch the consistency guarantee appear and vanish, the ring that places and replicates keys, vector clocks that tell a stale write from a genuine conflict, hinted handoff that keeps you writable when a node dies, Merkle trees that repair divergence cheaply, and the CAP framing that explains why Dynamo chose availability.
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