Caching Strategy

Table of Contents

• Overview
• When to Use
• Quick Start
• Reference Guides
• Best Practices

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