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

99 practiced

Explain and sketch an implementation of an approximate LFU eviction policy suitable for very large caches (for example TinyLFU using a Count-Min Sketch plus an LRU window). Describe the main data structures, how admission and eviction decisions are made, update costs on access, memory overhead, and error characteristics under skewed workloads.

MediumTechnical

80 practiced

You want to measure the real-world impact of adding a caching layer on user-facing latency and backend load. Design an experiment (canary or A/B) and list the metrics to collect (client p50/p95/p99, backend QPS/CPU, cache hit ratio), sampling method, how you'd ensure statistical significance, and how to attribute improvements specifically to caching.

HardSystem Design

139 practiced

Design an event-driven cache invalidation protocol across multiple services using a message system like Kafka. Include event schema (what fields), guarantees you require (ordering or at-least-once), idempotency strategies, handling out-of-order or late events, and how consumers should apply invalidations to avoid windows of inconsistency.

EasyTechnical

82 practiced

Describe common cache eviction policies (LRU, LFU, FIFO, and TTL-based expiry). For each policy explain: how it decides what to evict, what access patterns it suits best, and give a short concrete example scenario (e.g., session caching, hot-working set, analytics counters).

EasyTechnical

74 practiced

Explain the cache-aside (lazy loading) pattern. Provide step-by-step flows for a cache read miss and for a write operation. Discuss typical failure modes you must handle in a backend implementation (examples: origin read fails, cache write fails, race between concurrent misses).

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