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Key-Value Store System Design
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Scenario
A single hot partition holds 20% of traffic while other nodes sit idle—your "distributed" KV store just became a expensive Redis. The interview probes consistent hashing, replication, quorum reads/writes, and conflict resolution when nodes fail mid-request, not whether you can spell CAP.
Design a distributed key-value store that partitions data across many nodes, replicates for durability, and serves get/put/delete at massive scale. Naive mod-N hashing reshuffles everything when clusters grow; production systems use consistent hashing, quorum protocols, and explicit failure handling.
You should support horizontal scale, replication, tunable consistency, and recovery after node loss. Be ready to explain partitioning, read/write quorums, hinted handoff, and conflict resolution under partition. Hot keys and anti-entropy are common follow-ups.
Constraints
get/put/delete by key, TTL optional, replication factor N, tunable consistency per request
99.99% availability, < 10 ms p99 get on warm path, partition tolerance, automatic failover
Petabytes total, billions of keys, millions of ops/s aggregate, 100+ nodes per cluster, skewed key access
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