Does MySQL Work With Redis?
MySQL and Redis work together seamlessly in a complementary architecture where MySQL handles persistent storage and Redis serves as a high-performance cache layer.
Quick Facts
How MySQL Works With Redis
MySQL and Redis are designed to work together as part of a layered architecture pattern. MySQL acts as your source of truth for persistent data, while Redis sits in front as a cache to dramatically reduce database load and improve response times. When a request comes in, your application checks Redis first; on cache misses, it queries MySQL and populates Redis with the result. This approach is language-agnostic and doesn't require direct integration between the databases—the application layer orchestrates the interaction. Many frameworks like Laravel, Django, and Node.js have built-in support for this pattern through ORMs and cache adapters. The real benefit appears at scale: frequently accessed data can be served from Redis's in-memory store (microsecond latencies) instead of hitting disk I/O on MySQL. You'll also use Redis for session storage, rate limiting, and job queues alongside the caching layer. The main architectural consideration is handling cache invalidation correctly—stale data is the primary risk when combining these systems.
Best Use Cases
Quick Setup
npm install mysql2 redis ioredisconst redis = require('redis');
const mysql = require('mysql2/promise');
const redisClient = redis.createClient({ host: 'localhost', port: 6379 });
const mysqlPool = mysql.createPool({ host: 'localhost', user: 'root', password: 'pass', database: 'app' });
async function getUserById(userId) {
const cacheKey = `user:${userId}`;
// Try Redis first
const cached = await redisClient.get(cacheKey);
if (cached) return JSON.parse(cached);
// Cache miss: fetch from MySQL
const conn = await mysqlPool.getConnection();
const [rows] = await conn.query('SELECT * FROM users WHERE id = ?', [userId]);
conn.release();
if (rows.length) {
const user = rows[0];
// Store in cache for 1 hour
await redisClient.setEx(cacheKey, 3600, JSON.stringify(user));
return user;
}
return null;
}
getUserById(123).then(user => console.log(user));Known Issues & Gotchas
Cache invalidation becomes complex with multiple application instances
Fix: Implement cache key versioning, use pub/sub for invalidation broadcasts, or leverage TTLs with a refresh strategy
Redis data loss on restart if persistence is disabled
Fix: Enable RDB snapshots or AOF persistence depending on durability requirements, or treat Redis as purely ephemeral
Memory limits reached without monitoring
Fix: Set maxmemory policies, monitor Redis memory usage, implement LRU eviction policies, and size Redis appropriately for your working set
Inconsistent reads if cache TTL is too long
Fix: Balance TTL values based on data freshness requirements; consider event-driven invalidation for critical data
Alternatives
- •PostgreSQL with pg-boss and Memcached: Swap MySQL for PostgreSQL with better JSON support and use Memcached instead of Redis
- •DynamoDB with ElastiCache: AWS-native alternative combining DynamoDB's managed persistence with ElastiCache for caching
- •MongoDB with Redis: Document database instead of relational, paired with Redis for cache layer
Resources
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