Caching Strategies Explained with Interview Examples

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Interview Examples: Real Questions from Tech Interviews

Caching is a staple in FAANG interviews, often tied to system design or coding problems. Below are 30+ high-quality questions reportedly asked at companies like Google, Amazon, and Meta, with in-depth answers drawing from real experiences (e.g., from GitHub repos, Devinterview.io, and Glassdoor reports). These go beyond basics, testing depth.

1. Define caching in computer programming. Caching stores frequently accessed data in fast memory to reduce retrieval time from slower sources. It improves performance by exploiting temporal and spatial locality. For example, in a web app, caching user profiles avoids repeated DB queries.
2. What are the main purposes of caching? Primary goals: enhance speed (reduce latency), cut costs (fewer DB hits), scale systems (handle more traffic), and improve reliability (serve data during outages). Stats: Caching can boost throughput by 5-10x in read-heavy apps.
3. Explain cache hit and miss. Hit: Data found in cache (fast access). Miss: Not found, fetch from source (slower). Types of misses: compulsory (first access), capacity (cache full), conflict (mapping issues). To minimize, use larger caches or better policies.
4. Impact of cache size on performance? Larger caches increase hit rates but raise costs and potential latency from searches. Optimal size balances hit rate (e.g., 95%) with memory use—use formulas like Belady’s anomaly for prediction.
5. Difference between local and distributed caching? Local: Per-instance, fast but not shared (e.g., Guava). Distributed: Across nodes, scalable but networked (e.g., Redis). Use local for low-latency, distributed for high-availability.
6. Common cache eviction strategies? LRU (remove least recent), LFU (least frequent), FIFO (oldest first). LRU is popular for temporal locality; LFU for frequency-based workloads.
7. What is a cache key? Unique identifier for cached data, often hashed (e.g., URL + params). Good keys ensure collisions are rare and eviction is fair.
8. Importance of cache expiration? Prevents stale data; managed via TTL. Without it, caches grow indefinitely, leading to OOM errors.
9. How does cache invalidation work? Remove or update entries on data change. Methods: manual (e.g., @CacheEvict in Spring), time-based, or event-driven.
10. Steps to implement a basic cache? Use a map for storage, add get/put methods, implement eviction (e.g., LRU with LinkedHashMap in Java).
11. Handle cache sync in distributed env? Use pub/sub (Redis) or consensus protocols (Zookeeper) for invalidation broadcasts.
12. Role of hash maps in caches? O(1) access for keys; extended with doubly-linked lists for LRU.
13. Caching algorithms and differences? Direct-mapped (fast, high conflicts), set-associative (balance), fully-associative (flexible, slow).
14. Design for high concurrency? Use thread-safe structures (ConcurrentHashMap), locks, or sharding.
15. Prevent cache stampede? Use probabilistic early expiration or locking on misses.
16. Trade-offs: read vs write-heavy? Read-heavy: Favor read-through. Write-heavy: Write-back for speed, but risk inconsistency.
17. Cache manifest in web apps? Lists resources for offline caching (Service Workers in PWA).
18. Ensure cache-DB consistency? Write-through or invalidation events.
19. Cache tagging benefits? Group-related items for bulk invalidation (e.g., all “user:123” tags).
20. Distributed caching advantages? Scalability, redundancy over local; handles failures via replication.
21. Handle partitioning in distributed? Consistent hashing to minimize reshuffling on node changes.
22. Consistency models? Eventual (delayed sync, high avail), strong (immediate, low perf).
23. Cache replication? Master-slave or peer-to-peer; sync via gossip protocols.
24. Avoid coherence issues? Use write-invalidate or write-update protocols.
25. Network latency in distributed? Locality-aware placement, compression, batching.
26. Challenges in distributed cache? Partitioning, failure detection, hot spots—mitigate with auto-scaling.
27. Write-through in distributed? Cache nodes write to shared DB synchronously.
28. Handle node failures? Replication and failover; quorum reads/writes.
29. Shared vs distributed cache? Shared: Single instance (bottleneck). Distributed: Multi-node (scalable).
30. CDN and caching relation? CDNs cache at edges for low-latency global delivery.
31. Edge caching use cases? Static assets in web apps; reduces origin load.
32. Cache warming? Pre-populate cache on startup; for predictable queries.
33. Query caching in DBs? Store results; invalidate on table changes.
34. Object caching in OOP? Cache instances; reduces creation overhead.
35. Caching in microservices? Per-service caches with central invalidation.
36. Serverless caching? Use managed services like AWS ElastiCache.

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Conclusion

Caching strategies are the unsung heroes of performant systems, turning potential bottlenecks into seamless experiences. By mastering these, you’ll not only optimize your code but ace interviews too. Ready to apply this? Experiment with a small project—implement cache-aside in your app and measure the gains. What’s your go-to caching strategy? Share in the comments!

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