A distributed database and a traditional relational database differ primarily in their architecture and how they handle data storage and access. A traditional relational database is designed to operate on a single server or instance, where data is stored in structured tables with defined schemas. This means that data is centrally managed, and typical operations, such as queries and updates, are performed locally. In contrast, a distributed database is spread across multiple locations or nodes. Each node can be a separate server or even a group of servers, and the data can be partitioned or replicated across these nodes to assure availability and fault tolerance.
In a traditional relational database, performance and scalability can become bottlenecks as the database size grows or as more users access it simultaneously since all requests must go through a single point. In such systems, scaling often requires upgrading the server’s hardware (vertical scaling), and may result in downtime. Distributed databases, however, can scale horizontally by adding more servers or nodes to the system. This means that as demand increases, new nodes can be added to handle the load without significant disruption. For example, a distributed database like Apache Cassandra allows for quick scaling by simply adding new nodes to the cluster, which can efficiently share the data workload.
Additionally, the way data consistency is managed differs between the two systems. Traditional relational databases typically focus on ACID (Atomicity, Consistency, Isolation, Durability) properties, ensuring strong consistency across transactions. This means when a transaction completes, all users will see the same data immediately. Conversely, distributed databases often adopt different consistency models, such as eventual consistency, which allows for temporary mismatches across nodes but ultimately reconciles to a consistent state over time. Systems like Amazon DynamoDB exemplify this approach, ensuring quicker responses and availability especially in geo-distributed environments, where network latency can be an issue.