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Caching Strategies and Patterns Questions

Comprehensive knowledge of caching principles, architectures, patterns, and operational practices used to improve latency, throughput, and scalability. Covers multi level caching across browser or client, edge content delivery networks, application in memory caches, dedicated distributed caches such as Redis and Memcached, and database or query caches. Includes cache design and selection of technologies, defining cache boundaries to match access patterns, and deciding when caching is appropriate such as read heavy workloads or expensive computations versus when it is harmful such as highly write heavy or rapidly changing data. Candidates should understand and compare cache patterns including cache aside, read through, write through, write behind, lazy loading, proactive refresh, and prepopulation. Invalidation and freshness strategies include time to live based expiration, explicit eviction and purge, versioned keys, event driven or messaging based invalidation, background refresh, and cache warming. Discuss consistency and correctness trade offs such as stale reads, race conditions, eventual consistency versus strong consistency, and tactics to maintain correctness including invalidate on write, versioning, conditional updates, and careful ordering of writes. Operational concerns include eviction policies such as least recently used and least frequently used, hot key mitigation, partitioning and sharding of cache data, replication, cache stampede prevention techniques such as request coalescing and locking, fallback to origin and graceful degradation, monitoring and metrics such as hit ratio, eviction rates, and tail latency, alerting and instrumentation, and failure and recovery strategies. At senior levels interviewers may probe distributed cache design, cross layer consistency trade offs, global versus regional content delivery choices, measuring end to end impact on user facing latency and backend load, incident handling, rollbacks and migrations, and operational runbooks.

HardTechnical
79 practiced
In a system with caches, a primary database, and a search index (e.g., Elasticsearch), describe common consistency pitfalls when updating entities (for example user profile changes). Propose an ordered update workflow that minimizes stale reads across layers and supports failure recovery, and explain trade-offs involved.
MediumTechnical
69 practiced
Compare Redis, Memcached, and in-process application caches (e.g., Guava Cache or LRU maps) across these criteria: persistence, replication, data structures, scalability, operational complexity, and common use-cases. For a small startup building social feeds, which would you choose and why?
HardTechnical
99 practiced
You need to migrate a monolithic app using an in-process LRU cache to a shared Redis cluster without downtime and with minimal origin load. Describe a step-by-step migration plan covering namespace/key naming, dual-read or dual-write strategies, cache warming, blue-green deployment considerations, and rollback steps if errors occur.
EasyTechnical
133 practiced
Explain the differences between Least Recently Used (LRU) and Least Frequently Used (LFU) eviction policies. For an in-memory cache storing user session objects that are frequently accessed soon after login then rarely, which policy is more appropriate and why?
HardTechnical
76 practiced
Deep dive: design a secure caching policy for sensitive PII that must be cached for performance in encrypted form, support per-tenant isolation in a multi-tenant Redis cluster, and allow fast revocation. Cover encryption-at-rest/in-transit, key management and rotation, tenant key scoping (namespaces/prefixes), and revocation mechanisms that avoid full cache flush.

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