Document databases support horizontal scaling by allowing you to distribute data across multiple servers, rather than storing all data on a single machine. This means that as your data volume grows, you can simply add more servers to accommodate the increased load. Each server stores a portion of the entire database, making it easier to manage large datasets and high traffic loads. By spreading the data, you can improve both the performance and availability of applications that rely on this data.
One key feature of document databases that aids in horizontal scaling is their schema-less structure. In document databases, each record (or document) can have a different structure, which allows for flexibility in data representation. This makes it easier to partition data based on various criteria, such as user location or data type. For instance, in a social media application, user profiles can be stored in one set of servers while posts and comments can reside on others. This separation enables efficient use of resources and better load balancing among the servers.
Another advantage is the design of query processing in document databases. Many of these databases allow for local queries to be processed at the server level where the data resides, minimizing the need for data to travel over the network. This approach reduces latency and speeds up response times, even as the database grows. Technologies like sharding are commonly used in document databases, where data is divided into smaller, manageable pieces that can be independently scaled. By implementing sharding, a document database can efficiently handle large volumes of read and write operations, making it suitable for modern applications with variable workloads.