Document databases store data in a flexible, structured format that resembles a document, usually in JSON or BSON (Binary JSON) format. Unlike traditional relational databases, which organize data into tables and rows, document databases group related information into a single document. Each document can have a different structure, allowing developers to store semistructured data easily. This flexibility makes it easier to accommodate changes in the data structure without needing a complex migration process.
Internally, document databases often use collections to group similar documents. Each document in a collection can include nested data, lists, and various data types, which helps represent complex relationships naturally. For example, a single document could encapsulate an entire user profile with personal details, preferences, and user activity history, all wrapped up together. This design allows for more intuitive data retrieval since all relevant information is located within a single document, reducing the need for expensive joins or complex queries that span multiple tables.
Another key feature of document databases is their ability to scale horizontally. This means that as data grows, more servers can be added to distribute the load instead of relying on a single, more powerful machine. Many document databases offer built-in mechanisms for replication and sharding, allowing for improved performance and availability. Popular document databases like MongoDB and Couchbase exemplify this approach, allowing developers to build applications that can easily adapt to varying data needs while maintaining efficient access and retrieval capabilities.