Database Scaling Strategies: From Single Server to Global Distribution

Phase 1: Query Optimization

Before adding infrastructure, optimize what you have. Most performance issues are solved by better indexes and queries.

CREATE INDEX idx_orders_customer_status ON orders(customer_id, status, created_at DESC);
CREATE INDEX idx_products_category_price ON products(category_id, price);

Phase 2: Read Replicas

For read-heavy workloads, add read replicas to distribute the load.

class DatabaseService {
  async getOrder(id) {
    return this.readDb.order.findUnique({ where: { id } });
  }
  async createOrder(data) {
    return this.writeDb.order.create({ data });
  }
}

Phase 3: Caching Layer

Cache hot data to reduce database load. Use Redis with appropriate TTL and invalidation strategies.

async getUser(id) {
  const cached = await redis.get(`user:${id}`);
  if (cached) return JSON.parse(cached);
  const user = await db.user.findUnique({ where: { id } });
  await redis.setex(`user:${id}`, 3600, JSON.stringify(user));
  return user;
}

Phase 4: Sharding

When a single server can't handle the write load, split data across multiple servers.

Decision Framework

1. Optimize queries and add indexes first
2. Add read replicas for read-heavy workloads
3. Cache hot data with Redis
4. Shard only when nothing else works