Document databases scale horizontally by distributing data across multiple servers or nodes, allowing for increased storage and query capacity without needing to upgrade to a larger single machine. This approach contrasts with vertical scaling, where a single server is upgraded with more resources. With horizontal scaling, as the demand for data grows, additional servers can simply be added to the system, enabling the database to handle more extensive datasets and support more concurrent users efficiently.
To implement horizontal scaling in document databases, data is typically partitioned or sharded. Each shard contains a subset of the data, and the database management system (DBMS) takes care of determining where specific documents are stored. For instance, in a user-based application, you might split documents using a hash based on user IDs. Different servers can then store documents for different users, ensuring that no single server is overwhelmed by requests for data. Technologies like MongoDB and Couchbase make it straightforward to set up sharding, and they have built-in mechanisms to manage how data is balanced across multiple nodes.
Another key aspect of horizontal scaling in document databases is their ability to replicate data across these multiple servers for redundancy and fault tolerance. This ensures that if one node goes down, another can take over without data loss. Data replication might involve maintaining multiple copies of the same documents on different nodes. By using techniques like master-slave replication or multi-master setups, document databases can ensure high availability while providing read-heavy applications with improved performance. Overall, horizontal scaling provides a flexible way for document databases to grow alongside the needs of applications.