A relational database handles scalability primarily through vertical scaling and horizontal scaling strategies. Vertical scaling involves increasing the resources of a single database server, such as adding more CPU, memory, or faster storage. This is often simpler to implement because it requires fewer changes to the application or its architecture. However, there's a limit to how much you can scale a single server, and as the load increases, it may eventually become insufficient for handling peak traffic and data storage requirements.
Horizontal scaling, on the other hand, distributes the database load across multiple servers. This can be done through techniques like sharding, where data is divided into smaller segments and each segment is stored on a different server. For example, a large e-commerce application might store customer data on one server and order data on another, allowing more efficient access and reducing the load on any single server. Additionally, some relational databases provide built-in support for clustering, which allows for multiple instances of the database to work together and share the load while maintaining consistency and high availability.
It's also important to consider how a relational database manages connections and queries during scaling. Connection pooling can help manage multiple connections effectively, reducing the overhead that comes with opening and closing connections frequently. As the database scales, developers can optimize their queries and use indexing to maintain performance. Overall, while relational databases may have inherent limitations compared to some NoSQL solutions, they can still be effectively scaled to accommodate growing applications when employing a combination of vertical and horizontal strategies.