A distributed key-value store answers the single-node limits of capacity, throughput, availability and durability with three layers stacked on each other.
Spread keys across nodes with consistent hashing (lessons 0003–0004), so capacity and throughput scale by adding machines and no single node holds everything.
Copy each key to N nodes, the next N distinct servers clockwise on the ring. Now a node loss costs durability nothing, and any replica can serve a read.
Quorums make consistency a dial. With W + R > N the read and write sets overlap, so reads see the
latest write. Lower W or R for faster, more available operations that may return stale data. Most
large stores set this for AP: stay up during partitions, reconcile after.
Three mechanisms keep an AP store honest without a central coordinator.
One write path touches every one of them.
Together these make the Dynamo-style AP design the default for session caches and shopping-cart stores, where staying available matters more than a moment of staleness.
Source: Alex Xu, System Design Interview Vol 1, Ch. 6
Answer to reveal the explanation. Nothing is scored.
1During a network partition, what do DynamoDB and Cassandra choose in CAP terms?
They pick availability and partition tolerance: keep serving reads and writes and reconcile later, rather than blocking during a partition.
2With N replicas, when does W + R > N guarantee a read sees the latest write?
If the write set (W) and the read set (R) must overlap, which is what W + R > N forces, then at least one replica in every read saw the most recent write.
3A write's target node is down. What is hinted handoff?
A neighbor holds a 'hint' for the down node and replays the write once it recovers. The write stays available through the outage.