Data modeling in MongoDB is the process of designing and creating the structure of collections and documents that will be stored in the database.
Maintaining data in an organized manner is very important for database efficiency. It also ensures data security, data accuracy, and better functioning. To maintain an organized database, it is important to learn data modeling.
In this article, we will go through MongoDB data modeling with examples and explore MongoDB’s features and capabilities.
What is Data Modeling in MongoDB?
MongoDB Data modeling is the process of arranging unstructured data from a real-world event into a logical data model in a database.
There is no need to build a schema before adding data to MongoDB database because MongoDB is flexible. This means that MongoDB supports a dynamic database schema.
To create a perfect Data model in MongoDB, always balance application needs, database engine performance features, and data retrieval patterns.
When creating data models, always account for both the application uses of the data, such as queries, updates, and data processing, as well as the fundamental design of the data itself.
MongoDB Data Model Designs
For modeling data in MongoDB, two strategies are available. These strategies are different and it is recommended to analyze scenario for a better flow.
The two methods for data model design in MongoDB are:
- Embedded Data Model
- Normalized Data Model
1. Embedded Data Model
This method, also known as the de-normalized data model, allows you embedd all of the related documents in a single document.
These nested documents are also called sub-documents.
Embedded Data Model example
If we obtain student information in three different documents, Personal_details, Contact, and Address, we can embed all three in a single one, as shown below.
{
_id: ,
Std_ID: "987STD001"
Personal_details:{
First_Name: "Rashmika",
Last_Name: "Sharma",
Date_Of_Birth: "1999-08-26"
},
Contact: {
e-mail: "rashmika_sharma.123@gmail.com",
phone: "9987645673"
},
Address: {
city: "Karnataka",
Area: "BTM2ndStage",
State: "Bengaluru"
}
}
2. Normalized Data Model
In a normalized data model, object references are used to express the relationships between documents and data objects. Because this approach reduces data duplication, it is relatively simple to document many-to-many relationships without having to repeat content.
Normalized data models are the most effective technique to model large hierarchical data with cross-collection relationships.
Normalized Data Model Example
Here we have created multiple collections for storing students data which are linked with _id.
Student:
{
_id: <StudentId101>,
Std_ID: "10025AE336"
}
Personal_Details:
{
_id: <StudentId102>,
stdDocID: " StudentId101",
First_Name: "Rashmika",
Last_Name: "Sharma",
Date_Of_Birth: "1999-08-26"
}
Contact:
{
_id: <StudentId103>,
stdDocID: " StudentId101",
e-mail: "rashmika_sharma.123@gmail.com",
phone: "9987645673"
}
Address:
{
_id: <StudentId104>,
stdDocID: " StudentId101",
city: "Karnataka",
Area: "BTM2ndStage",
State: "Bengaluru"
}
Advantages Of Data Modeling in MongoDB
Data modeling in MongoDB is essential for a successful application, even though at first it might just seem like one more step. In addition to increasing overall efficiency and improving development cycles, data modeling helps you better understand the data at hand and identify future business requirements, which can save time and money.
In particular, applying suitable data models:
- Improves application performance through better database strategy, design, and implementation.
- Allows faster application development by making object mapping easier.
- Helps with better data learning, standards, and validation.
- Allows organizations to assess long-term solutions and model data while solving not just current projects but also future application requirements, including maintenance.
Considerations While Creating Data Model Design in MongoDB
Some important points to consider while creating a data model for MongoDB database are:
- Design the database according to user preference and common applications.
- Create separate collections and insert unique documents in them.
- Combine documents in a collection, if they will be used frequently together.
- Do joins while write, not on read.
- Use complex aggregation in database.
- Duplicate data (in a limit), as disc spaceis cheaper than computational time.
Conclusion
MongoDB Data modeling is a fundamental component of database development. In this article, we explored the best practices for designing a highly effective and scalable data model in MongoDB.
We also distinguished between embedded data model and normalized data model to ensure that we choose the appropriate one for our application. We can create effective data models if we keep all of these things in mind.
MongoDB Data Modeling – FAQs
What is data modeling in MongoDB?
Data modeling in MongoDB is the process of designing the structure and organization of data within a MongoDB database.
What are the different types of data modeling?
There are three types of data modeling:
- conceptual model
- logical model
- physical data model
What is an example of data modeling?
An example of data modelling can be seen in a blogging application. Data modeling in MongoDB could involve embedding comments within a blog post document to optimize read performance and enable atomic write operations, while storing user data in a separate collection for normalization and reusability.
What are the benefits of MongoDB data modeling?
MongoDB data modeling offers several benefits as follows:
- Flexibility
- Faster Development
- Scalability
- Geospatial Data
- Horizontal Scaling
Is MongoDB a multi model?
MongoDB is not typically considered a multi-model database. It is primarily known as a document-oriented NoSQL database.
What is model and schema in MongoDB?
In MongoDB, a schema defines the structure of a document or record in a collection, while a model provides an interface to the database for CRUD operations.