Joins vs embedding decision in MongoDB - Quick Revision & Key Differences

Recall & Review

beginner

What is embedding in MongoDB?

Embedding means storing related data inside a single document as nested objects or arrays. It keeps related information together for fast access.

beginner

What is a join in MongoDB?

A join combines data from two or more collections using the $lookup stage in aggregation. It links documents based on a shared key.

intermediate

When should you choose embedding over joins?

Choose embedding when related data is mostly read together, changes rarely, and the document size stays within limits. It improves read speed by avoiding joins.

intermediate

When is using joins ($lookup) better than embedding?

Use joins when related data changes often, is large, or is shared across many documents. Joins keep data normalized and avoid duplication.

advanced

What is a key limitation of embedding in MongoDB?

Embedded documents increase the size of a single document, which can hit MongoDB's 16MB document size limit and slow updates if data changes frequently.

Which MongoDB feature allows combining data from multiple collections?

A indexing

B embedding

C sharding

D $lookup

$lookup is the aggregation stage used to perform joins between collections.

Embedding is best when:

A Related data is read together and rarely changes

B Data changes frequently and is large

C Data is shared across many documents

D You want to avoid document size limits

Embedding is ideal when related data is accessed together and does not change often.

What is a risk of embedding too much data in one document?

A Slower joins

B Data duplication

C Hitting the 16MB document size limit

D Lack of indexes

MongoDB limits document size to 16MB, so embedding large data can exceed this limit.

Which approach helps avoid data duplication in MongoDB?

A Embedding

B Joins using $lookup

C Storing all data in one collection

D Using large arrays

Joins keep data normalized and avoid duplicating shared data.

If related data is updated frequently and shared by many documents, you should:

A Use $lookup joins

B Duplicate the data

C Embed it inside each document

D Avoid storing it

Using joins keeps data consistent and avoids duplication when updates are frequent.

Explain the main factors to consider when deciding between embedding and using joins in MongoDB.

Think about how often you read and update related data and how big your documents can be.

Describe a real-life example where embedding is better than joins, and another example where joins are better.

Consider when data is tightly connected vs. when data is shared or large.

Practice

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

easy

A. When related data is large and changes frequently

B. When related data is frequently accessed together and rarely changes

C. When data needs to be shared across many documents

D. When you want to enforce strict relational constraints

Solution

1. Step 1: Understand embedding use case

   Embedding stores related data inside one document for fast access and atomic updates.

2. Step 2: Match scenario to embedding benefits

   If data is accessed together and rarely changes, embedding avoids extra lookups and is efficient.

3. Final Answer:

   When related data is frequently accessed together and rarely changes -> Option B

4. Quick Check:

   Embedding = fast access, rare changes [OK]

Hint: Embed when data is read together and changes rarely [OK]

Common Mistakes:

• Embedding large, frequently changing data
• Embedding data shared across many documents
• Confusing embedding with referencing

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

easy

A. { user: { $ref: 'users', $id: ObjectId('abc123') } }

B. { embedded_user: { name: 'Alice' } } inside the document

C. { user_id: ObjectId('abc123') } inside the document

D. { user: 'Alice' } as a string

Solution

1. Step 1: Identify referencing syntax

   Referencing stores the ObjectId of another document to link collections.

2. Step 2: Match correct reference format

   Storing the ObjectId directly (e.g., user_id: ObjectId('abc123')) is the standard referencing method.

3. Final Answer:

   { user_id: ObjectId('abc123') } inside the document -> Option C

4. Quick Check:

   Reference = store ObjectId [OK]

Hint: Reference by storing ObjectId, not embedding full data [OK]

Common Mistakes:

• Embedding full document instead of referencing
• Using deprecated $ref and $id fields
• Storing plain strings instead of ObjectId

3. Given two collections: orders with embedded items array, what is the main benefit of embedding items inside orders?

medium

A. Faster retrieval of all items for an order without extra queries

B. Ability to reuse items across multiple orders easily

C. Smaller document size for orders collection

D. Enforcing foreign key constraints automatically

Solution

1. Step 1: Understand embedding effect on queries

   Embedding items inside orders means all item data is in one document.

2. Step 2: Identify benefit of embedding items

   This allows fetching an order and its items in a single query, improving speed.

3. Final Answer:

   Faster retrieval of all items for an order without extra queries -> Option A

4. Quick Check:

   Embedding = single query fetch [OK]

Hint: Embedding avoids extra queries for related data [OK]

Common Mistakes:

• Thinking embedding reduces document size
• Assuming embedded data can be reused easily
• Expecting automatic foreign key enforcement

4. You have a MongoDB schema where user profiles embed their addresses. You notice address updates are frequent and slow. What is the best fix?

medium

A. Switch to referencing addresses in a separate collection

B. Embed more fields inside the address document

C. Increase the document size limit

D. Add indexes on embedded address fields

Solution

1. Step 1: Identify problem with embedding frequent updates

   Embedding addresses means updating user documents often, which can be slow and large.

2. Step 2: Choose solution for frequent changing data

   Referencing addresses separately allows updating addresses independently without rewriting user documents.

3. Final Answer:

   Switch to referencing addresses in a separate collection -> Option A

4. Quick Check:

   Frequent updates = use referencing [OK]

Hint: Use referencing for frequently updated data [OK]

Common Mistakes:

• Adding indexes without fixing schema design
• Embedding more fields increases document size
• Increasing document size limit doesn't improve update speed

5. You design a blogging platform where posts have comments. Comments can be many and users want to edit them independently. Which design is best?

hard

A. Embed all comments inside each post document

B. Store comments as plain text fields inside post

C. Embed only the latest comment inside post, others referenced

D. Store comments in a separate collection and reference post ID

Solution

1. Step 1: Analyze comment characteristics

   Comments can be many and need independent editing, so they change often and grow large.

2. Step 2: Choose schema design for many, editable comments

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

3. Final Answer:

   Store comments in a separate collection and reference post ID -> Option D

4. Quick Check:

   Many editable items = referencing best [OK]

Hint: Many changing items = use referencing, not embedding [OK]

Common Mistakes:

• Embedding many comments causes large documents
• Embedding only latest comment complicates queries
• Storing comments as plain text fields loses structure