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

query-caching-strategies

Query Caching Strategies

Table of Contents

Overview

When to Use