MongoDB Relationships: Embedded vs. Referenced | Tutorial 2025

Data relationships are fundamental to database design. Choosing the right way to represent relationships impacts performance, scalability, and data consistency. In MongoDB, a NoSQL document database, you have two primary options for modeling relationships: embedded documents and referenced documents. This tutorial delves into both methods, providing practical examples and guiding you to make informed decisions for your MongoDB projects in 2025 and beyond.

Table of Contents

1. Introduction to MongoDB Relationships
1. Why Data Relationships Matter
2. Embedded vs. Referenced: A High-Level Overview
2. Embedded Documents
1. What are Embedded Documents?
2. Benefits of Embedding
3. Drawbacks of Embedding
4. When to Use Embedding: Use Cases
5. Embedding in Practice: Examples with Code
3. Referenced Documents
1. What are Referenced Documents?
2. Benefits of Referencing
3. Drawbacks of Referencing
4. When to Use Referencing: Use Cases
5. Referencing in Practice: Examples with Code
4. Embedded vs. Referenced: A Detailed Comparison
1. Data Duplication
2. Query Performance
3. Data Consistency
4. Atomicity
5. Scalability
6. Complexity
5. Advanced Considerations
1. Database Normalization in MongoDB (or Lack Thereof)
2. Denormalization Strategies
3. Considerations for Large Datasets
4. Impact of Data Updates
6. Practical Examples and Scenarios
1. Scenario 1: E-Commerce Product Catalog
2. Scenario 2: Social Media Posts and Comments
3. Scenario 3: User Profiles and Preferences
7. Best Practices for Choosing a Relationship Model
1. Analyze your Query Patterns
2. Consider Data Size and Growth
3. Think about Data Consistency Requirements
4. Evaluate Update Frequency
8. MongoDB Atlas Considerations
1. Atlas Search and Relationship Modeling
2. Performance Monitoring in Atlas
9. Tools and Techniques for Managing Relationships
1. MongoDB Compass
2. Aggregation Framework
3. Data Modeling Tools
10. Conclusion: Making the Right Choice for Your Data
11. Further Learning and Resources

1. Introduction to MongoDB Relationships

1.1 Why Data Relationships Matter

In any database system, relationships between data entities are critical for representing real-world connections. For example, a customer has orders, an article has comments, and a book has authors. The way you model these relationships significantly affects:

• Data Integrity: Ensuring that data is consistent and accurate.
• Query Performance: How quickly you can retrieve related data.
• Scalability: The ability of your database to handle increasing amounts of data and traffic.
• Maintainability: How easy it is to update and modify your data structure.

Choosing the appropriate relationship model is, therefore, a fundamental decision in database design.

1.2 Embedded vs. Referenced: A High-Level Overview

MongoDB offers two primary ways to model relationships:

• Embedded Documents: Store related data within a single document. This is sometimes referred to as denormalization.
• Referenced Documents: Store related data in separate documents and use references (typically IDs) to link them. This aligns more with relational database normalization.

Let’s explore each approach in detail.

2. Embedded Documents

2.1 What are Embedded Documents?

Embedded documents involve including related data directly within a single document. This means you’re essentially nesting one document inside another.

Example: Consider a blog post and its comments. With embedding, each blog post document would contain an array of comment documents.

2.2 Benefits of Embedding

• Improved Read Performance: Fetching related data requires a single database read, which can significantly speed up queries. This eliminates the need for joins, which can be expensive.
• Simplified Queries: Retrieve all necessary information in one go.
• Data Locality: Related data is stored together on disk, potentially improving caching and overall performance.

2.3 Drawbacks of Embedding

• Data Duplication: If the same data is embedded in multiple documents, it can lead to redundancy. This increases storage space and the risk of inconsistencies.
• Limited Flexibility: Updating embedded data across multiple documents can be complex and inefficient.
• Document Size Limitations: MongoDB has a maximum document size (currently 16MB). Embedding excessively large amounts of data can hit this limit.
• Complex Updates: Updating a deeply nested document can be cumbersome and require complex update operations.

2.4 When to Use Embedding: Use Cases

Embedding is most suitable when:

• One-to-one or one-to-many relationships exist, and the “many” side is small and doesn’t grow excessively.
• You frequently need to retrieve the related data together.
• You rarely need to update the embedded data independently.
• Data consistency is not a paramount concern, or you can manage it programmatically.
• Read performance is critical.

Examples include:

• Address embedded within a user profile.
• Order items embedded within an order document.
• Comments embedded within a blog post (for a blog with relatively few comments per post).

2.5 Embedding in Practice: Examples with Code

Let’s illustrate embedding with some MongoDB examples using the MongoDB shell.

Scenario: Embedding addresses in a user document.

First, let’s create a user document with an embedded address:

  db.users.insertOne({
    username: "johndoe",
    email: "john.doe@example.com",
    address: {
      street: "123 Main St",
      city: "Anytown",
      state: "CA",
      zip: "91234"
    }
  })
  

To retrieve a user’s information along with their address:

  db.users.findOne({ username: "johndoe" })
  

The result will include the embedded address document within the user document.

Scenario: Embedding comments in a blog post.

  db.posts.insertOne({
    title: "MongoDB Relationships Tutorial",
    content: "This is a tutorial on MongoDB relationships.",
    comments: [
      {
        author: "alice",
        text: "Great tutorial!"
      },
      {
        author: "bob",
        text: "Very helpful, thank you."
      }
    ]
  })
  

Retrieving the blog post with its comments:

  db.posts.findOne({ title: "MongoDB Relationships Tutorial" })
  

Again, the comments array will be embedded within the post document.

Updating Embedded Documents:

To update the embedded address, you would use the positional operator $ or dot notation.

  db.users.updateOne(
    { username: "johndoe" },
    { $set: { "address.city": "Springfield" } }
  )
  

This updates the city in the embedded address document for the user “johndoe”.

3. Referenced Documents

3.1 What are Referenced Documents?

Referenced documents involve storing related data in separate collections and using references (usually the _id field) to link them. This approach mirrors the relational database concept of foreign keys.

Example: Instead of embedding comments within a blog post, you would store comments in a separate “comments” collection and include the blog post’s _id in each comment document.

3.2 Benefits of Referencing

• Reduced Data Duplication: Data is stored only once, minimizing redundancy and inconsistencies.
• Improved Data Consistency: Changes to shared data are reflected everywhere, ensuring consistency.
• Greater Flexibility: Easier to update related data independently.
• Scalability: Handles larger datasets and complex relationships more effectively.
• Normalization Benefits: Mimics normalization principles, making data management cleaner in complex scenarios.

3.3 Drawbacks of Referencing

• Increased Read Complexity: Requires multiple queries to retrieve related data, potentially impacting performance.
• More Complex Queries: You need to use techniques like $lookup in aggregations or multiple find() operations to retrieve related data.
• Potentially Slower Read Performance: Joining data across collections can be slower than retrieving embedded data.

3.4 When to Use Referencing: Use Cases

Referencing is most suitable when:

• Many-to-many relationships exist.
• The “many” side of a one-to-many relationship can grow very large.
• You frequently need to update the related data independently.
• Data consistency is critical.
• Storage space optimization is a primary concern.
• Complex data models with multiple relationships exist.

Examples include:

• Products and categories in an e-commerce system (many products can belong to multiple categories).
• Users and roles in an access control system.
• Books and authors (a book can have multiple authors, and an author can write multiple books).
• Comments and blog posts (especially when posts have a large number of comments).

3.5 Referencing in Practice: Examples with Code

Let’s illustrate referencing with MongoDB examples.

Scenario: Referencing categories from product documents in an e-commerce system.

First, create the “categories” collection:

  db.categories.insertMany([
    { _id: 1, name: "Electronics" },
    { _id: 2, name: "Clothing" },
    { _id: 3, name: "Books" }
  ])
  

Now, create product documents that reference the categories:

  db.products.insertMany([
    {
      name: "Laptop",
      category_ids: [1], // Reference to Electronics category
      price: 1200
    },
    {
      name: "T-Shirt",
      category_ids: [2], // Reference to Clothing category
      price: 25
    },
    {
      name: "MongoDB: The Definitive Guide",
      category_ids: [3], // Reference to Books category
      price: 40
    }
  ])
  

To retrieve a product’s category information, you would typically use the $lookup aggregation stage. This performs a left outer join between the “products” and “categories” collections.

  db.products.aggregate([
    {
      $lookup: {
        from: "categories",
        localField: "category_ids",
        foreignField: "_id",
        as: "categories"
      }
    },
    {
      $match: { name: "Laptop" }
    }
  ])
  

This aggregation pipeline will retrieve the “Laptop” product document and include an array of category documents that match the category_ids in the product document.

Scenario: Referencing authors from book documents.

  db.authors.insertMany([
    { _id: 1, name: "John Steinbeck" },
    { _id: 2, name: "Jane Austen" }
  ])

  db.books.insertMany([
    {
      title: "The Grapes of Wrath",
      author_ids: [1], // Reference to John Steinbeck
      publication_year: 1939
    },
    {
      title: "Pride and Prejudice",
      author_ids: [2], // Reference to Jane Austen
      publication_year: 1813
    }
  ])
  

To retrieve a book’s author information:

  db.books.aggregate([
    {
      $lookup: {
        from: "authors",
        localField: "author_ids",
        foreignField: "_id",
        as: "authors"
      }
    },
    {
      $match: { title: "The Grapes of Wrath" }
    }
  ])
  

Updating Referenced Documents:

Updates are simpler with referencing as you only need to update the relevant document in its respective collection. For example, to change the name of a category:

  db.categories.updateOne(
    { _id: 1 },
    { $set: { name: "Consumer Electronics" } }
  )
  

This change will be reflected in all products referencing that category without needing to update each product individually.

4. Embedded vs. Referenced: A Detailed Comparison

Let’s compare the two approaches based on several key factors.

4.1 Data Duplication

• Embedded: High data duplication potential.
• Referenced: Minimal data duplication.

4.2 Query Performance

• Embedded: Generally faster for reads, as all data is in one document.
• Referenced: Slower reads, as requires joining data from multiple collections (using $lookup or multiple queries).

4.3 Data Consistency

• Embedded: Maintaining consistency can be challenging. Changes to shared data require updating multiple documents.
• Referenced: Easier to maintain consistency, as data is stored in one place.

4.4 Atomicity

• Embedded: Atomic updates are possible within a single document.
• Referenced: Atomic updates across multiple collections are more complex and may require transactions (available in MongoDB 4.0 and later).

4.5 Scalability

• Embedded: Can be problematic for very large datasets due to document size limitations and update complexity.
• Referenced: Scales better for large datasets and complex relationships.

4.6 Complexity

• Embedded: Simpler for simple relationships and reads, but can become complex for updates and large datasets.
• Referenced: More complex queries and requires understanding of aggregation and joining, but simplifies updates and data management in complex scenarios.

Summary Table:

5. Advanced Considerations

5.1 Database Normalization in MongoDB (or Lack Thereof)

MongoDB is a NoSQL database, and while the concept of “normalization” isn’t directly applicable in the same way as in relational databases, the principles are still relevant. Referencing encourages a more normalized approach, reducing data redundancy and improving data consistency. Embedding, on the other hand, embraces denormalization.

The key is to understand the trade-offs and choose the approach that best fits your specific requirements.

5.2 Denormalization Strategies

If you choose embedding (denormalization), consider these strategies to mitigate the drawbacks:

• Limit Embedding Depth: Avoid deeply nested documents, as they can be difficult to query and update.
• Controlled Duplication: Only duplicate data that is frequently accessed together and rarely changes.
• Application-Level Consistency: Implement logic in your application to ensure data consistency when updates are necessary.
• Consider Change Streams: Use MongoDB’s Change Streams to monitor changes to embedded data and propagate updates to other relevant documents. This is especially useful for maintaining eventual consistency.

5.3 Considerations for Large Datasets

For very large datasets, referencing generally scales better than embedding. Consider these points:

• Sharding: MongoDB’s sharding capabilities can distribute data across multiple servers, improving performance and scalability. Referencing works well with sharding, as related data can be distributed across different shards.
• Indexing: Proper indexing is crucial for query performance, especially when using referencing and the $lookup aggregation stage.
• Data Archiving: Periodically archive old or infrequently accessed data to reduce the size of your active dataset.

5.4 Impact of Data Updates

Think about how frequently your data will be updated. Frequent updates to embedded data can lead to performance issues and data inconsistency. Referencing simplifies updates, as you only need to update the relevant document in its own collection.

6. Practical Examples and Scenarios

Let’s analyze a few practical scenarios to illustrate the choice between embedding and referencing.

6.1 Scenario 1: E-Commerce Product Catalog

Entities: Products, Categories, Manufacturers, Reviews

Relationships:

• A product belongs to one or more categories (many-to-many).
• A product is made by one manufacturer (one-to-one or one-to-many).
• A product has many reviews (one-to-many).

Recommendation:

• Categories: Use referencing (separate “categories” collection). Products can have an array of category IDs. This is because a product can belong to multiple categories, and categories can change independently.
• Manufacturer: Use referencing (separate “manufacturers” collection) unless manufacturer data is very small and rarely changes.
• Reviews: A hybrid approach. For products with a small number of reviews, embedding might be acceptable. However, for products with a large number of reviews, referencing is more scalable and prevents the product document from becoming too large. Consider pagination of reviews in the application layer.

6.2 Scenario 2: Social Media Posts and Comments

Entities: Posts, Comments, Users

Relationships:

• A post is created by one user (one-to-one or one-to-many).
• A post has many comments (one-to-many).
• A comment is created by one user (one-to-one or one-to-many).

Recommendation:

• Comments: Referencing is generally the better choice. Social media posts can have a very large number of comments. Embedding all comments within the post document would lead to performance issues and document size limitations.
• Users: Referencing for both posts and comments.

6.3 Scenario 3: User Profiles and Preferences

Entities: Users, Preferences

Relationships:

• A user has one set of preferences (one-to-one).

Recommendation:

• Preferences: Embedding is a good choice. User preferences are typically small and rarely change. Embedding simplifies queries and improves read performance.

7. Best Practices for Choosing a Relationship Model

Here are some best practices to guide your decision-making process.

7.1 Analyze your Query Patterns

Understand how your data will be queried. If you frequently need to retrieve related data together, embedding might be a good choice. If you often need to query data independently, referencing is more appropriate.

7.2 Consider Data Size and Growth

Estimate the size of your data and how it will grow over time. If the “many” side of a relationship is likely to grow very large, referencing is generally a better choice.

7.3 Think about Data Consistency Requirements

Determine how important data consistency is for your application. If consistency is critical, referencing is the preferred approach.

7.4 Evaluate Update Frequency

Assess how frequently your data will be updated. Frequent updates to embedded data can lead to performance issues. Referencing simplifies updates.

8. MongoDB Atlas Considerations

MongoDB Atlas, the cloud database service, offers additional features that can influence your relationship modeling choices.

8.1 Atlas Search and Relationship Modeling

Atlas Search allows you to build powerful full-text search indexes on your data. It can be used effectively with both embedded and referenced data. For referenced data, you may need to use aggregation pipelines to join the data before indexing.

8.2 Performance Monitoring in Atlas

Atlas provides comprehensive performance monitoring tools that can help you identify and address performance bottlenecks related to your relationship models. Use these tools to track query performance, identify slow queries, and optimize your data model.

9. Tools and Techniques for Managing Relationships

Several tools and techniques can help you manage relationships in MongoDB.

9.1 MongoDB Compass

MongoDB Compass is a GUI tool that provides a visual interface for exploring your data, creating queries, and managing your database. It can be helpful for understanding and managing relationships between documents.

9.2 Aggregation Framework

The aggregation framework is a powerful tool for transforming and analyzing data in MongoDB. It’s essential for working with referenced data, as it allows you to join data from multiple collections using the $lookup stage.

9.3 Data Modeling Tools

Several data modeling tools can help you design your MongoDB schema and visualize relationships between documents. These tools can make it easier to understand and communicate your data model.

10. Conclusion: Making the Right Choice for Your Data

Choosing between embedded and referenced documents in MongoDB requires careful consideration of your specific requirements. There’s no one-size-fits-all answer. Analyze your query patterns, data size, consistency needs, and update frequency to make an informed decision. Remember that you can also use a hybrid approach, combining embedding and referencing to optimize your data model for specific use cases.

By understanding the benefits and drawbacks of each approach, you can design a MongoDB database that is performant, scalable, and easy to maintain.

11. Further Learning and Resources

• MongoDB Official Documentation
• MongoDB University
• MongoDB Blog