Caching Strategies for High-Traffic Systems

Why caching matters

As your traffic grows, every database query or API response adds latency and cost. Caching allows you to serve data faster, scale horizontally, and create resilient architectures that handle millions of requests without breaking a sweat.

1. Client-Side Caching

The browser is your first cache layer. Use Cache-Control headers, ETag, and localStorage/sessionStorage to avoid reloading static data unnecessarily.

Cache-Control: public, max-age=3600
ETag: "user-profile-123"

2. CDN Edge Caching

Use a Content Delivery Network (CDN) like Cloudflare, Fastly, or AWS CloudFront to cache assets and API responses at global edge locations. This reduces round-trip latency and protects your servers from traffic bursts.

• Cache static files: images, scripts, fonts.
• Cache full HTML pages (for non-personalized views).
• Use “stale-while-revalidate” to refresh content asynchronously.

3. Application-Level Caching

Cache expensive computations or database results in-memory using tools like Redis, Memcached, or in-process caches like LRU.

// Example using Redis with Node.js
const cacheKey = "users:list";

const cached = await redis.get(cacheKey);
if (cached) return JSON.parse(cached);

const users = await db.query("SELECT * FROM users LIMIT 100");
await redis.set(cacheKey, JSON.stringify(users), "EX", 60); // cache 1 min
return users;

4. Database Query Caching

Many databases like MySQL, PostgreSQL, and MongoDB have internal caching mechanisms. For analytics or repetitive queries, materialized views or query result caches can dramatically improve performance.

5. API Gateway Caching

Layer 7 gateways (e.g., NGINX, API Gateway, Kong) can cache upstream responses for frequent requests. They offload backend services and can serve traffic even if a downstream service is under maintenance.

proxy_cache_path /data/nginx/cache levels=1:2 keys_zone=my_cache:10m;
location /api/ {
  proxy_cache my_cache;
  proxy_cache_valid 200 1m;
  proxy_pass http://backend;
}

6. Layered Caching Strategy

Combine caching at multiple layers — CDN + API + database — to handle both static and dynamic workloads.

• Edge cache — static assets & API GET responses.
• Redis — hot data or computation results.
• Database cache — views or read replicas.

7. Cache Invalidation & TTL

“There are only two hard things in Computer Science: cache invalidation and naming things.” A cache is only as good as its invalidation logic. Use short TTLs or event-based invalidation.

await redis.del("users:list"); // invalidate when new user added

8. Consistency Considerations

Caches introduce potential staleness. For critical data (like payments), prefer read-through or write-through caches over lazy invalidation.

• Write-through: writes go to cache and DB simultaneously.
• Read-through: cache fetches from DB on misses.
• Write-behind: async write to DB after cache update (faster but risky).

9. Observability & Cache Metrics

Track cache hit ratio, evictions, and latency. Low hit ratios or frequent evictions indicate poor cache design or insufficient memory.

10. Caching in Microservices

In distributed systems, cache coherence becomes tricky. Use message queues (e.g., Kafka) or pub/sub patterns to sync invalidations across services.