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A document data model organizes application data into structured records made of named fields and values. A record can hold simple values alongside nested objects and arrays, bringing related information together in a shape suited to how an application uses it. In this database context, a “document” is a data record—not necessarily a PDF or office file.
What is a document data model?
The phrase has two closely related meanings: the structure an application gives its data as document-shaped records, and the broader approach used by a document-oriented database. In either sense, the central unit is a document: a logical record made up of fields and their values.
For example, an application might represent a customer order with an order number, date, and an array of line items. Each line item could itself be a nested object with a product name, quantity, and price. The exact fields depend on the application, and this example is illustrative rather than a prescribed schema.
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Fields, values, and nested data
Fields give values names, so applications can work with meaningful properties rather than an undifferentiated block of data. Values may be simple, such as text or a number, or more complex, such as a nested document or an array. MongoDB describes a document as its basic data unit and stores documents in BSON, a binary representation of JSON documents that supports additional data types: MongoDB’s Documents documentation.
Flexible structure, not absent structure
Documents within a collection do not always need identical fields or types. That flexibility can help an application evolve as its requirements change. It does not mean that data has no structure: the application still defines what fields mean, which values it expects, and how it handles missing or inconsistent data. Couchbase likewise describes a flexible schema that applications can evolve over time in its Data Model documentation.
How to design a document data model
Start with the application’s read and update patterns, then decide which information belongs together and where separate references make more sense. MongoDB’s modeling guidance recommends storing data accessed together together, which can reduce the need to gather related values from multiple records. That is a design principle, not a rule that every related item should be embedded in one document.
- Identify the application’s common operations. Determine which information is typically read or changed together.
- Choose document boundaries. Group data that is naturally used together, while considering whether parts need to be handled separately.
- Define and maintain the structure. Specify expected fields and values, and plan how the application will handle changes or variation.
- Check the model against its relationships and updates. A suitable design depends on the application’s access patterns and relationship needs, not on flexibility alone.
For detailed modeling principles, see MongoDB’s Data Modeling documentation.
How document databases differ in implementation
“Document data model” describes a general way of organizing data, but it does not guarantee identical behavior across database products. Storage formats, identifiers, and update rules are implementation details.
- MongoDB: Documents use BSON, and standard collections include an
_idfield, as described in its Documents documentation. - Apache CouchDB: Documents have unique names, and updates use an optimistic model in which a save can encounter a conflict if another client has saved first. See the CouchDB Technical Overview.
These are product-specific examples, not universal properties of every document database.
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What the term does not imply
- A document is not necessarily a word-processing file, PDF, or arbitrary attachment; here it means a structured database record.
- A flexible schema does not remove the need for application-defined structure and data-quality rules.
- Embedding related data is not always the right choice; the application’s access patterns and relationship requirements determine the boundaries.
- The term alone does not establish that a database will be faster, easier to scale, or better suited to every application.
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