Caching Strategies
Master caching hierarchies, Redis vs Memcached, cache eviction policies (LRU, LFU, TTL), write-through vs write-back patterns, cache stampede prevention, and real-world cache invalidation strategies.
Master caching hierarchies, Redis vs Memcached, cache eviction policies (LRU, LFU, TTL), write-through vs write-back patterns, cache stampede prevention, and real-world cache invalidation strategies. This hands-on tutorial focuses on practical implementation of caching strategies concepts.
Caching Strategies
Caching is the single most impactful performance optimization in system design. A well-placed cache can reduce database load by 99%, cut latency from 100ms to 1ms, and allow a system to serve 100x more traffic with the same infrastructure.
[!IMPORTANT] Caching is a trade-off: you trade memory for speed and simplicity for performance. Every cache decision involves answering: "Can we tolerate stale data here, and for how long?"
The Cache Hierarchy
Caches exist at every level of the stack, each with different speeds and capacities:
In system design interviews, "caching" almost always refers to distributed caching (Redis/Memcached) and CDN caching.
Cache Eviction Policies
When a cache is full, the eviction policy determines which item gets removed to make room for new data:
Write Policies
How data flows between your cache and database determines consistency and write performance:
| Policy | Consistency | Write Speed | Data Loss Risk | Use Case |
|---|---|---|---|---|
| Write-Through | ✅ Strong | Slower (2 writes) | None | User profiles, payment records |
| Write-Back | ⚠️ Eventual | Fastest | Cache crash = data loss | Game leaderboards, analytics |
| Write-Around | ✅ Strong | Fast (1 write) | None | One-time write data, logs |
| Cache-Aside | ✅ Strong | Fast (lazy) | None | General-purpose reads |
Cache-Aside (Lazy Loading) Pattern
The most common pattern: the application manages the cache manually:
Cache Stampede Prevention
When a popular cached item expires, thousands of concurrent requests hit the database simultaneously:
[!TIP] Redis vs Memcached in one sentence: Use Redis if you need persistence, pub/sub, Lua scripts, or complex data structures (lists, sorted sets, hashes). Use Memcached only if you need raw key-value speed with horizontal scaling simplicity.
Quiz
Quiz
Question 1 of 3A social media app shows a user's follower count. The count can be slightly outdated for up to 60 seconds. Which cache write policy is most appropriate?
Key Takeaways
✅ Cache-Aside (Lazy Loading) is the most common pattern — populate on miss, invalidate on write.
✅ LRU is the default eviction policy; use LFU for content with viral/popularity patterns.
✅ Write-Through ensures consistency; Write-Back maximizes write throughput at the cost of potential data loss.
✅ Cache stampede is a real production risk — use mutex locks or probabilistic early refresh to prevent it.
✅ TTL is mandatory — never cache without an expiry; stale data is often worse than a cache miss.
Official Resources
- 📖 Redis Documentation — Commands, data types, persistence, clustering
- 📖 Redis Eviction Policies — Official guide to all eviction modes
- 📖 Memcached vs Redis (AWS) — Side-by-side comparison
What's Next?
Caching reduces load on your origin servers — but what if you could serve content from a server that's physically closer to your users?
Next: CDN & Edge Computing — How Cloudflare, AWS CloudFront, and edge nodes slash global latency.