Caching and Performance Optimization Questions

Covers design and implementation of multi layer caching and end to end performance strategies for web and backend systems. Topics include client side techniques such as browser caching, service worker strategies, code splitting, and lazy loading for components images and data; edge and distribution techniques such as content delivery network design and caching of static assets; and server side and data layer caching using in memory stores such as Redis and Memcached, query result caching, and database caching patterns. Includes cache invalidation and coherence strategies such as time to live, least recently used eviction, cache aside, write through and write behind, and prevention of cache stampedes. Covers when to introduce caching and when not to, performance and consistency trade offs, connection pooling, monitoring and metrics, establishing performance budgets, and operational considerations such as cache warm up and invalidation during deploys. Also addresses higher level concerns including search engine optimization implications and server side rendering trade offs, and how performance decisions map to user experience and business metrics at senior levels.

EasyTechnical

35 practiced

Describe three clear scenarios where you would not introduce caching because the cost or risk outweighs the benefit. For each scenario explain the risks, consistency requirements, and alternative performance strategies you would recommend instead of caching.

HardTechnical

27 practiced

Describe and design an implementation of stale-while-revalidate (SWReval) across CDN and origin caches. Explain how you would implement it end-to-end, how you prevent a stampede of revalidations, and how you ensure origin doesn't get overloaded during large-scale revalidation events.

HardTechnical

28 practiced

Prepare a cost comparison between scaling the primary relational database vertically/horizontally versus investing in a distributed caching layer (Redis cluster) to offload reads. Include CAPEX/OPEX considerations, operational complexity, risk of data staleness, and how to model ROI for a customer evaluating both options.

MediumTechnical

28 practiced

Given the following schema: users(user_id PK), orders(order_id PK, user_id FK, total decimal, updated_at timestamp). Explain a query-result caching strategy for frequent queries returning a user's last 10 orders. Include cache key design, TTL recommendation, invalidation on updates, and how to handle cold users with sparse activity. Assume eventual staleness up to 5 seconds is acceptable.

HardTechnical

27 practiced

Compare Redis clustering and Memcached for a large scale read-heavy cache: cover replication, failover, persistence, eviction semantics, memory efficiency, client maturity, and operational trade-offs. Recommend one for a scenario with high read volume, occasional heavy writes, and requirement for simple key-value semantics.

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