Joins vs embedding decision in MongoDB - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to embed an address inside a user document in MongoDB.

MongoDB

db.users.insertOne({ name: "Alice", address: [1] })

Drag options to blanks, or click blank then click option'

AObjectId("507f1f77bcf86cd799439011")

B[ "123 Main St", "Townsville" ]

C{ street: "123 Main St", city: "Townsville" }

D"123 Main St, Townsville"

Attempts:

3 left

💡 Hint

Common Mistakes

Using an array instead of an object for the address.

Using a string instead of a structured object.

Referencing an ObjectId instead of embedding the data.

Embedding an address as a sub-document uses an object with fields like street and city.

2fill in blank

medium

Complete the code to perform a $lookup join between orders and customers collections.

MongoDB

db.orders.aggregate([{ $lookup: { from: "[1]", localField: "customerId", foreignField: "_id", as: "customerInfo" } }])

Drag options to blanks, or click blank then click option'

Aproducts

Baddresses

Corders

Dcustomers

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong collection name in the 'from' field.

Joining with the same collection instead of the related one.

The $lookup stage joins the orders collection with the customers collection using matching fields.

3fill in blank

hard

Fix the error in the query to embed multiple phone numbers inside a user document.

MongoDB

db.users.insertOne({ name: "Bob", phones: [1] })

Drag options to blanks, or click blank then click option'

A[ { type: "home", number: "123-4567" }, { type: "work", number: "987-6543" } ]

B{ home: "123-4567", work: "987-6543" }

C"123-4567, 987-6543"

DObjectId("507f1f77bcf86cd799439012")

Attempts:

3 left

💡 Hint

Common Mistakes

Using a single object instead of an array for multiple phones.

Using a string or ObjectId instead of structured data.

Embedding multiple phone numbers requires an array of objects, each with type and number fields.

4fill in blank

hard

Fill both blanks to write a $lookup that joins orders with products and filters only orders with quantity greater than 5.

MongoDB

db.orders.aggregate([{ $lookup: { from: "[1]", localField: "productId", foreignField: "_id", as: "productDetails" } }, { $match: { quantity: { [2]: 5 } } }])

Drag options to blanks, or click blank then click option'

Aproducts

B$gt

C$lt

Dcustomers

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong collection name in $lookup.

Using $lt instead of $gt for filtering.

The $lookup joins with the products collection. The $match filters orders where quantity is greater than 5 using $gt.

5fill in blank

hard

Fill all three blanks to create a query that embeds a review inside a product document only if the rating is 4 or higher.

MongoDB

db.products.updateOne({ _id: [1] }, { $push: { reviews: { userId: [2], rating: [3] } } })

Drag options to blanks, or click blank then click option'

AObjectId("507f1f77bcf86cd799439013")

BObjectId("507f1f77bcf86cd799439014")

C4

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using string literals instead of ObjectId for IDs.

Using a rating less than 4.

The product _id and userId are ObjectIds. The rating must be 4 or higher, so 4 is used here.

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