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.

HardSystem Design

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GraphQL APIs often return deeply nested, personalized payloads. Design a caching approach for a GraphQL server that maximizes reuse: describe persisted queries, response fingerprinting, partial-field caching, query whitelisting, cache key strategies (root-level vs field-level), and how you'd handle authorization (Vary behavior).

MediumTechnical

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How would you measure and report cache warm-up time and its effect on user latency during deployments? Describe instrumentation (synthetic traffic, canary metrics), key metrics to capture (origin QPS, cache hit ratio over time, user p95 latency), and a visualization you would create to track warm-up progress.

MediumTechnical

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You cache per-region copies of some data in regional Redis clusters. When a write occurs in region A, describe strategies to ensure other regions see the update quickly: remote invalidation, background replication, versioned keys, or TTLs. Discuss trade-offs between staleness, write amplification, and cross-region latency.

EasyTechnical

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Explain the cache-aside (lazy loading) and write-through caching patterns. For each pattern, describe the read and write flows, their consistency guarantees, where you'd use them, and trade-offs with respect to latency, durability, and complexity. Give a concrete example (e.g., product-detail reads and price updates) to illustrate when you'd pick one over the other.

HardTechnical

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Design an O(1) approximate LFU (Least Frequently Used) eviction policy for a cache supporting millions of keys. Describe the data structures and algorithms you would use (e.g., frequency buckets, decay on frequencies), how you approximate true frequency efficiently, and how you handle memory overhead. Explain why naive counting can be expensive and how to mitigate it.

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