Document databases handle relationships between documents primarily through embedded documents and references. Unlike relational databases that use foreign keys to connect tables, document databases allow developers to include related data within a single document or reference other documents as needed. This flexibility reflects the nesting capabilities of document models, making it easier to manage related information without the complexities of joins.
For example, consider a blog application where you have two collections: one for posts and another for comments. A developer might choose to embed comments directly within a post document. This means that whenever the post is retrieved, all its associated comments are readily available. This approach improves performance by minimizing the number of queries needed to gather related data. However, it is essential to consider the potential downsides, such as document size limits and complications associated with updating nested structures.
In other cases, developers might prefer to use references, especially when the related data is large or shared across multiple documents. For instance, instead of embedding a user's information in each post they comment on, a developer might store user IDs in the comments and maintain a separate user collection. When necessary, the application can query the user collection to fetch user details. This method promotes data normalization and keeps document sizes manageable, but it may introduce additional complexity when retrieving related information, as it requires multiple queries to various collections. Ultimately, the choice between embedding and referencing depends on the specific use cases and performance considerations for the application being built.