Caching Strategies and In Memory Storage Questions
Understanding caching mechanisms (HTTP caching, application-level caching with Redis/Memcached). Cache invalidation strategies, TTL, and when to cache. Performance implications.
HardSystem Design
43 practiced
Design a globally distributed caching solution for a multi-region service requiring sub-5ms reads for local traffic and aims for eventual consistency on writes. Describe placement of caches (edge, region-local caches), replication strategies, write propagation, conflict handling, failover, and cost vs latency trade-offs.
MediumTechnical
49 practiced
Implement a thread-safe memoization decorator in Python that caches function results with a TTL per key. Explain how you handle race conditions when multiple callers compute the same expired value, and provide example usage demonstrating caching and expiry behavior.
MediumSystem Design
76 practiced
Design a sharded cache topology for 200k QPS using Redis nodes. Explain key partitioning choices (consistent hashing vs modulo), how to handle rebalancing when nodes are added/removed, client-side vs proxy routing, replication/replica read strategies, and how to minimize key remapping and downtime.
HardTechnical
54 practiced
Implement a high-concurrency LRU cache in Java supporting get, put, and TTL per entry. Aim for minimal locking using ConcurrentHashMap for storage and a concurrent eviction structure. Provide a code outline, explain concurrency strategies to avoid global locks, and discuss trade-offs and pitfalls like memory visibility and order maintenance.
EasyTechnical
50 practiced
Explain common cache eviction policies (LRU, LFU, FIFO) with algorithmic intuition, typical implementations in caches, the memory/time trade-offs, and provide a short example workload where LFU outperforms LRU. Mention how approximations are used in systems like Redis.
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