We use analytics and advertising cookies to understand how the site is used and whether our ads on Facebook and Instagram work. They are set only if you accept. See our Privacy Policy for details.
Consistent hashing, eviction, replication, and what really happens when a single hot key takes down the cluster.
Design a distributed in-memory cache: a cluster of nodes that stores key-value pairs with sub-millisecond access latency, sharded across the fleet, with eviction, replication, and graceful failure handling. Clients call get(key) / set(key, value, ttl) / del(key); the system handles the routing, sharding, eviction, and failures invisibly.
This question is dense with specific algorithmic choices (consistent hashing, eviction, quorums) and is a favorite for distributed-systems-heavy roles. Strong candidates name the algorithms, explain the trade-offs, and discuss failure modes explicitly.
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 5 components explained · 6 deep dives · trade-off analysis · 8 common follow-up questions
Unlock it with a subscription.
Try everything free for 7 days. Cancel anytime.
7-day trial, then $8/mo - or $48/yr ($4/mo, save 50%). Cancel anytime.
The canonical bounded system design problem. Read-heavy, hot-key prone, and a great vehicle for hashing, caching, and capacity estimation.
The classic write-vs-read amplification trade-off. Push, pull, or hybrid fanout - and how to handle the celebrity user with 100M followers.
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.
Mock interview: design Distributed Cache →