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MongoDB data is particularly flexible, and compared with SQL databases, it does not need to define the structure of the table before inserting the data. The collection of MongoDB does not emphasize a fixed document structure. This flexibility makes it easy to map the document structure. Each document can map the objects it wants to express, even if the data is substantially different. In fact, in practice, documents under the same collection usually have a similar structure.

The main problem of MongoDB data modeling is to strike a balance between the requirements of the application, the performance characteristics of the database engine and the data retrieval model. To design a data model, always consider the data used by the application (queries, updates, data that needs to be processed, and so on) as well as the data structure itself.

Document structure

The key to designing the MongoDB data model is to consider the relationship between the document structure and the data represented by the application. There are two ways to express this relationship: references and embedded document (embedded documents).

Reference (References)

References (References) store relationships between data, including links from one document or references to another. In this way, the application solves the problem of accessing the associated data, which is generally the data model of the canonical data.

Embedded Data

Embedded documents capture the relationship between data by storing related data in a document structure. An MongoDB document can embed a document as a subdocument in a field or array of the current document. These non-normalized data models allow applications to retrieve and manipulate related data in a single database operation.

Atomicity of write operations

In MongoDB, the atomicity of write operations is limited to the document level, and no single write operation can automatically affect multiple documents or collections. The standardized embedded data model integrates all the associated data in one document to present the entity. This helps atomic write operations insert and update entity data in a write operation. Normalized data can separate data from multiple sets and require multiple write operations in non-atomic operations.

Then, a pattern that promotes atomic writing may limit the application's use of data, or it may limit the way the application can be modified. Atomicity considers the challenges of design patterns, balancing flexibility and atomicity.

Document increase

Updates such as adding elements to an array or adding new fields increase the size of the document. If the size of the document exceeds the space allocated for the document, MongoDB reallocates disk space. Considering the increase in space, canonical data should be standardized or used.

Data usage and performance

When designing a data model, consider how the application uses the database. For example, if your application uses only recently inserted documents, consider using the top collection (Capped Collections). If the application needs to read the collection frequently, adding an index can improve the efficiency of data query.

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