Query Caching Strategies

Table of Contents

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

Overview

Implement multi-level caching strategies using Redis, Memcached, and database-level caching. Covers cache invalidation, TTL strategies, and cache warming patterns.

When to Use

• Query result caching
• High-read workload optimization
• Reducing database load
• Improving response time
• Cache layer selection
• Cache invalidation patterns
• Distributed cache setup

Quick Start

Minimal working example:

// Node.js example with Redis
const redis = require("redis");
const client = redis.createClient({
  host: "localhost",
  port: 6379,
  db: 0,
});

// Get user with caching
async function getUser(userId) {
  const cacheKey = `user:${userId}`;

  // Check cache
  const cached = await client.get(cacheKey);
  if (cached) return JSON.parse(cached);

  // Query database
  const user = await db.query("SELECT * FROM users WHERE id = $1", [userId]);

  // Cache result (TTL: 1 hour)
  await client.setex(cacheKey, 3600, JSON.stringify(user));
  return user;
}

// Cache warming on startup
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

| Guide | Contents | |---|---| | Redis Caching with PostgreSQL | Redis Caching with PostgreSQL | | Memcached Caching | Memcached Caching | | PostgreSQL Query Cache | PostgreSQL Query Cache | | MySQL Query Cache | MySQL Query Cache | | Event-Based Invalidation | Event-Based Invalidation | | Time-Based Invalidation | Time-Based Invalidation, LRU Cache Eviction |

Best Practices

✅ DO

• Follow established patterns and conventions
• Write clean, maintainable code
• Add appropriate documentation
• Test thoroughly before deploying

❌ DON'T

• Skip testing or validation
• Ignore error handling
• Hard-code configuration values