A distributed SQL database is a type of database that spreads its data across multiple locations or servers while still allowing users to interact with it using SQL (Structured Query Language). This setup enables scalability and redundancy, meaning that as the application grows, the database can handle more requests without a significant drop in performance. Essentially, it combines the benefits of traditional SQL databases, like strong consistency and ACID (Atomicity, Consistency, Isolation, Durability) transactions, with the advantages of distributed systems, such as horizontal scaling and fault tolerance.
In a distributed SQL database, data is partitioned or sharded across different nodes. This means that rather than having a single database instance, the system divides the data into smaller pieces and stores these across various servers. For example, if a company has users spread globally, the database can be configured to keep user data closer to the users' geographical location, thus reducing latency. This partitioning is often transparent to developers, who can still write standard SQL queries without needing to worry about where the data is physically stored. Popular examples of distributed SQL databases include Google Spanner and CockroachDB, both of which allow for seamless scaling and high availability.
Another important aspect of distributed SQL databases is their ability to provide strong consistency, which is crucial for applications that require accurate and reliable data. This is achieved using techniques such as leader election and consensus protocols to ensure that all nodes in the database agree on the current state of the data. As a result, developers can build applications with confidence that transactions will be processed correctly, even in cases of network failures or server outages. Overall, distributed SQL databases offer a powerful solution for scenarios that demand both the familiarity of SQL with the resilience and scalability of distributed systems.