Distributed relational databases are designed to store and manage data across multiple servers or nodes while maintaining relational database features like structured schemas and SQL support. The main characteristics include data distribution, horizontal scalability, and mechanisms for ensuring consistency and availability. These systems aim to combine the benefits of traditional relational databases with the flexibility of distributed systems, addressing challenges like large-scale data handling and high availability.
First, distributed relational databases partition data across multiple nodes, often using techniques like sharding. Sharding splits a table into smaller chunks based on a key (e.g., user IDs or geographic regions) and distributes them to different nodes. For example, a user table might be split so that users in Europe are stored on one node and those in Asia on another. This allows parallel processing of queries, improving performance. Replication is another key aspect: data copies are stored on multiple nodes to ensure redundancy. Systems like CockroachDB or Google Spanner use replication to maintain availability even if some nodes fail. However, balancing consistency across replicas requires protocols like Raft or Paxos to synchronize updates.
Second, these databases support horizontal scalability, enabling them to handle increased workloads by adding more nodes rather than upgrading a single server. Traditional relational databases often hit performance limits with vertical scaling (e.g., adding CPU/RAM), but distributed systems can expand by distributing the load. For instance, Amazon Aurora allows read replicas to offload query traffic from the primary node. Query execution is optimized across nodes using distributed query planners, which break down SQL queries into tasks that run in parallel on relevant shards. Tools like Citus (a PostgreSQL extension) automate this process, allowing developers to work with familiar SQL syntax while the system handles distribution details.
Finally, distributed relational databases prioritize high availability and fault tolerance. They achieve this through automatic failover mechanisms and distributed transaction management. If a node fails, the system redirects requests to replicas without manual intervention. Transactions are managed using protocols like two-phase commit (2PC) to ensure atomicity across nodes. For example, a banking application updating balances across shards would use 2PC to confirm all nodes commit the change or roll back. However, this can introduce latency, so some systems offer tunable consistency—allowing developers to choose between strong consistency (e.g., immediate data accuracy) or eventual consistency (e.g., faster writes with temporary inconsistencies). These features make distributed relational databases suitable for applications requiring both scalability and reliability, such as e-commerce platforms or global financial systems.