Caching Strategy
Table of Contents
Overview
Implement effective caching strategies to improve application performance, reduce latency, and decrease load on backend systems.
When to Use
- Reducing database query load
- Improving API response times
- Handling high traffic loads
- Caching expensive computations
- Storing session data
- CDN integration for static assets
- Implementing distributed caching
- Rate limiting and throttling
Quick Start
Minimal working example:
import Redis from "ioredis";
interface CacheOptions {
ttl?: number; // Time to live in seconds
prefix?: string;
}
class CacheService {
private redis: Redis;
private defaultTTL = 3600; // 1 hour
constructor(redisUrl: string) {
this.redis = new Redis(redisUrl, {
retryStrategy: (times) => {
const delay = Math.min(times * 50, 2000);
return delay;
},
maxRetriesPerRequest: 3,
});
this.redis.on("connect", () => {
console.log("Redis connected");
});
this.redis.on("error", (error) => {
// ... (see reference guides for full implementation)Reference Guides
Detailed implementations in the references/ directory:
| Guide | Contents |
|---|---|
| Redis Cache Implementation (Node.js) | Redis Cache Implementation (Node.js) |
| Cache Decorator (Python) | Cache Decorator (Python) |
| Multi-Level Cache | Multi-Level Cache |
| Cache Invalidation Strategies | Cache Invalidation Strategies |
| HTTP Caching Headers | HTTP Caching Headers |
Best Practices
✅ DO
- Set appropriate TTL values
- Implement cache warming for critical data
- Use cache-aside pattern for reads
- Monitor cache hit rates
- Implement graceful degradation on cache failure
- Use compression for large cached values
- Namespace cache keys properly
- Implement cache stampede prevention
- Use consistent hashing for distributed caching
- Monitor cache memory usage
❌ DON'T
- Cache everything indiscriminately
- Use caching as a fix for poor database design
- Store sensitive data without encryption
- Forget to handle cache misses
- Set TTL too long for frequently changing data
- Ignore cache invalidation strategies
- Cache without monitoring
- Store large objects without consideration