Distributed relational databases are designed to store and manage data across multiple servers or locations while maintaining the relational structure and integrity of the data. One of their main characteristics is data distribution, which allows data to be partitioned or replicated across different nodes. This distribution helps improve performance, as queries can be processed in parallel across multiple servers. For instance, if a database holds user information for a global application, user data can be partitioned by geographic location, allowing the system to provide quicker access to users by keeping their data closer to them.
Another key characteristic is the consistency and integrity of data. Distributed relational databases often use protocols to ensure that all nodes agree on changes made to the data, adhering to the ACID properties—Atomicity, Consistency, Isolation, and Durability. These properties are essential for ensuring that transactions are handled correctly and that the database remains reliable. For example, if a financial application updates account balances, it must do so consistently across all nodes to prevent discrepancies, ensuring that data integrity is maintained throughout the system.
Finally, distributed relational databases typically emphasize fault tolerance. This means that if one server fails, the system can continue to operate without losing access to data. This is achieved through techniques such as data replication, where copies of the data are maintained on multiple nodes. In practical terms, if a server goes down, another node with a copy of the data can take over, ensuring that users can still access the database with minimal disruption. An example of this is databases like Google Cloud Spanner or Amazon Aurora, which are designed to provide high availability and resilience while managing relational data across distributed environments.