Document databases handle relationships between documents primarily through two approaches: embedding related data within documents or using references to link separate documents. Unlike relational databases, which enforce strict table structures and foreign keys, document databases provide flexibility in how relationships are modeled. The choice between embedding and referencing depends on factors like query patterns, data size, and update frequency.
In the embedding approach, related data is nested directly within a document. For example, a user document might include an embedded address object or an array of order objects. This works well for read-heavy scenarios where related data is frequently accessed together, as it avoids additional queries. However, embedding can lead to data duplication if the same information appears in multiple documents (e.g., a shared product description in multiple orders). Updates to duplicated data require modifying every affected document, which can be inefficient. Referencing, on the other hand, uses unique identifiers (like document IDs) to link documents. For instance, an order document might store a user_id field pointing to a separate user document. This avoids duplication but requires additional queries to retrieve related data. Some document databases, like MongoDB, offer tools like the $lookup operator to perform server-side joins between collections, though these are less performant than relational joins and should be used sparingly.
Developers must weigh trade-offs when choosing between these methods. Embedding simplifies reads but complicates updates and increases document size. Referencing keeps documents smaller and avoids duplication but adds query overhead. For example, a blog platform might embed comments within a post document if comments are always displayed with the post. If comments are managed independently, storing them as separate documents with a post_id reference might be better. Document databases don’t enforce referential integrity, so applications must handle orphaned references (e.g., deleting orders when a user is removed). Proper indexing and schema design based on access patterns are critical to balancing performance and maintainability.