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Distributed Cache System Design
Visual Problem Diagram
Scenario
A flash sale starts and one product id owns a single Redis shard—CPU looks fine while the database melts. The interview is consistent hashing, eviction, replication lag, and cache-aside invalidation, not memorizing RDB flags or opcode trivia.
Design a distributed caching system like Redis or Memcached that stores key-value data across many nodes with replication and eviction. Production pain is hot keys, invalidation, and wrong consistency assumptions—not drawing one cache box.
You should support get/set/TTL, atomic ops, horizontal scale, and failure recovery. Be ready to explain consistent hashing, cache-aside, eviction, and replication lag tradeoffs.
Constraints
get/set/delete, TTL, eviction when full, atomic increment, optional persistence snapshot
sub-1 ms p99 on hot GET path, 99.9% availability, partition tolerant, horizontal scale-out
10M ops/s aggregate, 100 GB+ per cluster, 100M keys, skewed access patterns
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