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

0 practiced

You're an SRE and dashboards show a sudden drop in Redis cache hit rate and a corresponding spike in DB load. Describe the immediate triage steps you would take (first 15 minutes), the commands/metrics you'd inspect, and the quick mitigations you might apply to reduce DB pressure.

HardBehavioral

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Tell me about a time (or describe a hypothetical plan) when you introduced caching to reduce latency and cost. Walk through the decision process, stakeholders, performance metrics before/after, risks you considered, and how you validated the change and handled regressions.

HardTechnical

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Case study: After introducing aggressive caching for content pages, the product team observes a 5% drop in ad impressions and revenue. As the SRE lead, describe how you would investigate root cause, identify whether caching is the cause, propose mitigations that balance performance and revenue, and how you'd validate fixes.

MediumSystem Design

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Design a multi-layer caching strategy for an e-commerce site that uses server-side rendering (SSR) for SEO and personalization for logged-in users. Requirements: 10M monthly users, highly dynamic pricing, search pages, and product detail pages. Describe client, edge (CDN), and origin cache layers, cache keys, invalidation approaches and trade-offs between freshness and latency.

HardTechnical

0 practiced

Explain Redis eviction policies (volatile-lru, allkeys-lru, volatile-ttl, volatile-random, allkeys-random, volatile-lfu, allkeys-lfu). For each, describe how it works, memory and CPU tradeoffs, and which policy you'd choose for a large cache with many short-lived keys.

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