Data Modeling Introduction

The key challenge in data modeling is balancing the needs of the application, the performance characteristics of the database engine, and the data retrieval patterns. When designing data models, always consider the application usage of the data (i.e. queries, updates, and processing of the data) as well as the inherent structure of the data itself.

Flexible Schema

Unlike SQL databases, where you must determine and declare a table’s schema before inserting data, MongoDB’s collections, by default, does not require its documents to have the same schema.

Document Structure

The key decision in designing data models for MongoDB applications revolves around the structure of documents and how the application represents relationships between data. MongoDB allows related data to be embedded within a single document.

Embedded Data

Embedded documents capture relationships between data by storing related data in a single document structure. MongoDB documents make it possible to embed document structures in a field or array within a document. These denormalized data models allow applications to retrieve and manipulate related data in a single database operation.

References

References store the relationships between data by including links or references from one document to another. Applications can resolve these references to access the related data. Broadly, these are normalized data models.

Atomicity of Write Operations

Single Document Atomicity

In MongoDB, a write operation is atomic on the level of a single document, even if the operation modifies multiple embedded documents within a single document.

Multi-Document Transactions

Starting in version 4.0, for situations that require atomicity for updates to multiple documents or consistency between reads to multiple documents, MongoDB provides multi-document transactions for replica sets.

Data Use and Performance

When designing a data model, consider how applications will use your database. For instance, if your application only uses recently inserted documents, consider using Capped Collections. Or if your application needs are mainly read operations to a collection, adding indexes to support common queries can improve performance.

Document Growth and MMAPv1

Some updates, such as pushing elements to an array or adding new fields, increase a document’s size.

For the deprecated MMAPv1 storage engine, if the document size exceeds the allocated space for that document, MongoDB relocates the document on disk. When using the deprecated MMAPv1 storage engine, growth consideration can affect the decision to normalize or denormalize data.