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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.

MediumSystem Design
0 practiced
Design a caching approach for inventory counts to minimize oversells during a high-traffic flash sale. Assume 100k TPS at peak, global distribution, and that inventory decrements must be accurate. Explain which operations to cache, when to bypass cache, and how to scale consistent decrements.
MediumSystem Design
0 practiced
Design a caching architecture for expensive analytics queries where results can be up to 5 minutes stale. Consider materialized views, result caching layers, cache invalidation on upstream changes, multi-tenancy isolation, and eviction strategies for large result sets.
MediumSystem Design
0 practiced
Design a multi-level caching architecture for an e commerce product page that serves 200K RPS globally across 10 regions. Requirements: p95 page load under 100ms, product metadata is read-heavy, inventory must be accurate at checkout, personalization required for logged-in users, and support for 100M SKUs. Sketch tiers, where to place caches, invalidation strategies, and consistency trade-offs.
HardTechnical
0 practiced
Explain consistent hashing with bounded loads to avoid node hotspots. Describe algorithms such as rendezvous hashing or power of two choices and how virtual nodes or weighted hashing reduce key movement on node membership changes.
MediumSystem Design
0 practiced
Design an event-driven cache invalidation system using Kafka for a read-heavy catalog service. Address ordering of events, idempotency, duplicate delivery, consumer lag, and backpressure so that invalidations reliably update caches without overwhelming downstream systems.

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