How does a relational database handle scalability?

Relational databases handle scalability through vertical and horizontal scaling strategies, each with distinct approaches. Vertical scaling involves upgrading a single server’s hardware (CPU, RAM, storage) to handle increased load. For example, a PostgreSQL database running on a cloud service like AWS RDS can be scaled vertically by moving to a larger instance type. While straightforward, this method has limits—hardware costs grow exponentially, and physical machines eventually hit performance ceilings. Vertical scaling is often used for short-term needs but isn’t sustainable for rapidly growing systems.

Horizontal scaling distributes data across multiple servers, which is more complex but avoids single-server bottlenecks. A common technique is sharding, where data is split into partitions (e.g., dividing user records by geographic region or hashing customer IDs). For instance, a global e-commerce platform might shard its orders table by country to isolate traffic. Another approach is read replication, where copies of the database handle read queries while writes go to a primary server. MySQL’s built-in replication allows developers to offload reporting queries to replicas, reducing strain on the primary node. However, horizontal scaling introduces challenges like maintaining consistency across shards and handling cross-node transactions.

Cloud-managed services simplify scaling by automating many tasks. Amazon Aurora, for example, scales storage automatically and supports read replicas with low replication lag. Similarly, Google Cloud SQL offers auto-scaling storage and failover capabilities. Developers must still design schemas and queries with scalability in mind—avoiding unnecessary joins across shards, using indexing strategically, and caching frequently accessed data. While relational databases aren’t as inherently scalable as NoSQL systems, these strategies allow them to support large applications when planned carefully. Trade-offs like eventual consistency or added complexity in application logic are often necessary to balance performance and reliability.