Relational databases handle distributed storage by using a combination of techniques designed to maintain data consistency and integrity across multiple locations. In a distributed setup, the data is spread across several servers or nodes, which can be located in different geographical regions. This division allows databases to enhance performance and provide better failover options. Data can be partitioned, replicated, or sharded depending on the application needs. For example, partitioning allows large tables to be split into smaller, manageable pieces based on certain criteria, such as ranges of values, which can help reduce query response times.
Replication is another key approach where copies of the same data are stored across different servers. This improves availability and fault tolerance, as if one server fails, other replicas can serve the requests without data loss. Many relational databases support synchronous or asynchronous replication strategies. In synchronous replication, all copies must be updated at the same time to ensure data consistency, while asynchronous replication allows updates to happen independently, which can improve speed at the risk of temporary data inconsistencies.
Finally, distributed relational databases often implement various strategies for managing transactions across nodes. This is crucial because maintaining the integrity of data is essential in a relational setting. Techniques like two-phase commit (2PC) are commonly used to ensure that all parts of a transaction either succeed together or fail together. For example, if a transaction modifies data in multiple locations, 2PC ensures that all servers agree on committing the changes. Tools and frameworks like Google Spanner and AWS Aurora are illustrative of how modern relational databases achieve distributed storage while still providing robust transactional support.