Embedding vs referencing decision in MongoDB

Introduction

Embedding and referencing help organize related data in MongoDB. Choosing between them affects how easy and fast it is to get your data.

When you want all related information together for quick access, like a blog post with its comments.

When data changes often and you want to avoid updating many places, like user profiles linked to orders.

When data is large or shared by many, so you want to keep it separate to save space, like product details used in many orders.

When you want to keep your data simple and avoid complex queries, embedding small related data helps.

When you expect to grow your data a lot, referencing helps keep things organized and scalable.

Syntax

MongoDB

Embedding example:
{
  _id: ObjectId("..."),
  name: "Alice",
  address: {
    street: "123 Main St",
    city: "Townsville"
  }
}

Referencing example:
{
  _id: ObjectId("..."),
  name: "Alice",
  address_id: ObjectId("...")
}

Address document:
{
  _id: ObjectId("..."),
  street: "123 Main St",
  city: "Townsville"
}

Embedding stores related data inside the same document.

Referencing stores related data in separate documents and links them by ID.

Examples

Embedding author details inside the book document for quick access.

MongoDB

{
  _id: 1,
  name: "Book",
  author: {
    name: "John Doe",
    age: 45
  }
}

Referencing author by ID to keep author data separate and reusable.

MongoDB

{
  _id: 1,
  name: "Book",
  author_id: 101
}

{
  _id: 101,
  name: "John Doe",
  age: 45
}

Sample Program

This example shows embedding author info inside a book document. When we get the book, we get author details too.

MongoDB

db.books.insertOne({
  _id: 1,
  title: "MongoDB Guide",
  author: {
    name: "Jane Smith",
    email: "[e-mail]"
  }
})

const book = db.books.findOne({_id: 1})
printjson(book)

Important Notes

Embedding is good for data that belongs together and changes together.

Referencing is better when data is large, shared, or changes independently.

Think about how you will read and update your data to choose the best method.

Summary

Embedding stores related data inside one document for fast access.

Referencing links documents by ID to keep data separate and reusable.

Choose embedding for small, tightly related data; choose referencing for large or shared data.

Practice

1. Which scenario is best suited for embedding related data in MongoDB?

easy

A. Storing a user's profile and their list of addresses when addresses are few and rarely shared.

B. Storing product details and linking to multiple categories by ID.

C. Storing large documents that are frequently updated independently.

D. Storing shared tags used by many blog posts.

Solution

1. Step 1: Understand embedding use case

   Embedding is best when related data is small and tightly coupled, like a user's addresses.

2. Step 2: Analyze options for embedding

   Storing a user's profile and their list of addresses when addresses are few and rarely shared fits because addresses are few and not shared, so embedding improves read speed.

3. Final Answer:

   Storing a user's profile and their list of addresses when addresses are few and rarely shared. -> Option A

4. Quick Check:

   Embedding = small, tightly related data [OK]

Hint: Embed when data is small and tightly linked [OK]

Common Mistakes:

• Embedding large or shared data causing duplication
• Referencing small, simple data unnecessarily

2. Which of the following is the correct way to reference a document in MongoDB?

easy

A. { "author_id": ObjectId("507f1f77bcf86cd799439011") }

B. { "author": { "$ref": "users", "$id": ObjectId("507f1f77bcf86cd799439011") } }

C. { "author": ObjectId("507f1f77bcf86cd799439011") }

D. { "author": "507f1f77bcf86cd799439011" }

Solution

1. Step 1: Identify referencing syntax

   Referencing stores the related document's ID as a field, usually named with _id suffix.

2. Step 2: Check options for correct ID reference

   { "author_id": ObjectId("507f1f77bcf86cd799439011") } correctly stores the ObjectId with a clear field name author_id.

3. Final Answer:

   { "author_id": ObjectId("507f1f77bcf86cd799439011") } -> Option A

4. Quick Check:

   Reference = store related document's ID [OK]

Hint: Reference by storing ObjectId in a field ending with _id [OK]

Common Mistakes:

• Using string instead of ObjectId for references
• Using $ref and $id without DBRef context
• Embedding full document instead of referencing

3. Given a MongoDB collection orders where each order embeds a small array of items, what is the expected result of querying an order by ID?

medium

A. Only the order document without any items data.

B. An error because embedded arrays cannot be queried.

C. The order document with all embedded items included in the result.

D. A list of item documents from a separate collection.

Solution

1. Step 1: Understand embedding query behavior

   When data is embedded, querying the parent document returns all embedded data inside it.

2. Step 2: Analyze query result for embedded items

   Querying by order ID returns the order document including the embedded items array.

3. Final Answer:

   The order document with all embedded items included in the result. -> Option C

4. Quick Check:

   Embedding returns full embedded data [OK]

Hint: Embedded data returns inside parent document query [OK]

Common Mistakes:

• Expecting separate documents for embedded data
• Thinking embedded arrays cause query errors
• Confusing referencing with embedding results

4. You have a MongoDB schema where user documents embed an array of posts. You notice performance issues when updating posts frequently. What is the likely cause and fix?

medium

A. Indexing embedded arrays causes slow updates; remove indexes.

B. Referencing posts causes slow updates; embed posts instead.

C. Using ObjectId for posts causes slow updates; use strings instead.

D. Embedding large arrays causes slow updates; switch to referencing posts separately.

Solution

1. Step 1: Identify embedding impact on updates

   Embedding large or frequently updated arrays causes the whole document to be rewritten, slowing updates.

2. Step 2: Choose referencing to improve update performance

   Referencing posts separately allows updating posts without rewriting the entire user document.

3. Final Answer:

   Embedding large arrays causes slow updates; switch to referencing posts separately. -> Option D

4. Quick Check:

   Large/frequent updates = use referencing [OK]

Hint: Large, frequently updated data? Use referencing [OK]

Common Mistakes:

• Embedding large arrays despite frequent updates
• Ignoring update performance impact
• Misunderstanding ObjectId role in updates

5. You are designing a MongoDB schema for a blogging platform. Each blog post has many comments, and comments can be edited or deleted independently. Also, comments can be very large in number. What is the best design choice?

hard

A. Embed all comments inside each blog post document.

B. Reference comments in a separate collection linked by post ID.

C. Embed only the first 10 comments and reference the rest.

D. Store comments as a JSON string inside the blog post.

Solution

1. Step 1: Analyze comment characteristics

   Comments are numerous, large, and updated independently, so embedding would cause large documents and slow updates.

2. Step 2: Choose referencing for scalability and independent updates

   Referencing comments in a separate collection allows efficient updates and avoids large blog post documents.

3. Final Answer:

   Reference comments in a separate collection linked by post ID. -> Option B

4. Quick Check:

   Large, independent data = referencing [OK]

Hint: Large, independent data? Reference separately [OK]

Common Mistakes:

• Embedding large, frequently updated arrays
• Mixing embedding and referencing without clear rules
• Storing JSON strings instead of structured data