A document database and a relational database differ primarily in how they structure and manage data. A relational database organizes data into tables with predefined columns and rows, where each row represents a record and columns define strict data types. Relationships between tables are established using foreign keys, and data is queried using SQL. In contrast, a document database stores data as flexible, self-contained documents—often in JSON or BSON format—where each document can have a unique structure. Relationships are typically handled by embedding related data directly within a document or through references, eliminating the need for complex joins.
The way data is queried and transactions are handled also differs significantly. Relational databases rely on SQL, which allows for complex queries involving joins across multiple tables and supports ACID (Atomicity, Consistency, Isolation, Durability) transactions to ensure data integrity. For example, a banking system might use SQL to calculate an account balance by joining transaction records with user data. Document databases, however, use query APIs tailored to their document structures, often sacrificing joins for simplicity and scalability. While some document databases support ACID transactions for single documents, cross-document transactions may be limited or require application-level handling. For instance, MongoDB added multi-document ACID support in later versions, but it’s less common than in relational systems.
Use cases often dictate which type of database is preferable. Relational databases excel in scenarios requiring complex queries, strict data consistency, and well-defined schemas—like financial systems or inventory management. Document databases are better suited for applications with evolving data models or hierarchical data, such as content management systems or user profiles where attributes vary widely. For example, an e-commerce product catalog in a document database might store each product as a document with nested specifications, while a relational database would split this data into separate tables (products, categories, specifications). The choice depends on balancing flexibility, scalability, and transactional needs.