Caching Strategy Implementer

Caching Strategy Implementer

Prerequisites & Dependencies

• Redis 7+ strongly recommended, or an in-memory cache (Node node-cache, Python functools.lru_cache)
• Language runtime: Node.js 18+, Python 3.10+, or Go 1.21+
• Optional: npm i ioredis / pip install redis / go-redis for Redis client

Execution Steps

1. Choose the caching strategy based on data access patterns: Cache-Aside for read-heavy, Write-Through for write-heavy, TTL/LRU for eviction policies
2. Set up the Redis (or LRU) client with connection pooling and default TTL (e.g., 300s for session data, 86400s for product catalogs)
3. Implement Cache-Aside: on read, check cache first; miss → fetch from DB → populate cache; on write → invalidate/update cache entry
4. Implement Write-Through: every write goes through cache, which synchronously writes through to the underlying DB
5. Configure eviction policies: maxmemory-policy allkeys-lru in Redis, or maxsize + ttl in Node/Python caches
6. Add cache warming for critical paths and monitor hit/miss ratios via Redis INFO stats or Prometheus metrics
7. Benchmark read/write latency with and without cache, iterate TTL/size values

// Cache-Aside pattern with ioredis (Node.js)
const Redis = require('ioredis');
const redis = new Redis();

async function getUser(id) {
  const cacheKey = `user:${id}`;
  const cached = await redis.get(cacheKey);
  if (cached) return JSON.parse(cached); // cache hit

  // cache miss → fetch from DB
  const user = await db.query('SELECT * FROM users WHERE id = $1', [id]);
  await redis.setex(cacheKey, 300, JSON.stringify(user)); // TTL 5min
  return user;
}

async function createUser(user) {
  const created = await db.query('INSERT INTO users ... RETURNING *');
  await redis.setex(`user:${created.id}`, 300, JSON.stringify(created)); // write-through style
  return created;
}