Why schema design matters in MongoDB - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to insert a document into the 'users' collection.

MongoDB

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

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

A"25"

B25

Cage

Dnull

Attempts:

3 left

💡 Hint

Common Mistakes

Putting numbers inside quotes makes them strings.

Using undefined variables instead of values.

The age should be a number, so 25 without quotes is correct.

2fill in blank

medium

Complete the code to find all documents where the age is greater than 30.

MongoDB

db.users.find({ age: { [1]: 30 } })

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

A$gt

B$eq

C$lt

D$ne

Attempts:

3 left

💡 Hint

Common Mistakes

Using $lt which means 'less than'.

Using $eq which means 'equal to'.

The operator $gt means 'greater than'.

3fill in blank

hard

Fix the error in the update command to set the user's city to 'Paris'.

MongoDB

db.users.updateOne({ name: "Bob" }, { [1]: { city: "Paris" } })

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

Aupdate

Bset

C$update

D$set

Attempts:

3 left

💡 Hint

Common Mistakes

Leaving out the dollar sign.

Using incorrect operator names.

The correct MongoDB operator to update fields is $set.

4fill in blank

hard

Fill both blanks to create a schema design that embeds an address inside a user document.

MongoDB

db.users.insertOne({ name: "Eve", address: { [1]: "123 Main St", [2]: "NY" } })

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

Astreet

Bcity

Cage

Dcountry

Attempts:

3 left

💡 Hint

Common Mistakes

Using unrelated fields like age or country here.

Mixing up field names.

Embedding address fields like street and city inside the user document is a common schema design.

5fill in blank

hard

Fill all three blanks to write a query that finds users older than 20 living in 'London'.

MongoDB

db.users.find({ [1]: { [2]: 20 }, address: { [3]: "London" } })

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

Aage

B$gt

Ccity

Dname

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong field names.

Using wrong operators.

The query filters users where age is greater than 20 and city is 'London'.

Practice

1. Why is schema design important in MongoDB?

easy

A. It helps organize data and improves query performance.

B. It enforces strict data types like SQL databases.

C. It automatically creates indexes for all fields.

D. It prevents any data duplication in collections.

Solution

1. Step 1: Understand MongoDB schema flexibility

   MongoDB allows flexible schemas but good design helps keep data organized.

2. Step 2: Recognize performance impact

   Proper schema design improves how fast queries run by structuring data well.

3. Final Answer:

   It helps organize data and improves query performance. -> Option A

4. Quick Check:

   Schema design = Organize + Performance [OK]

Hint: Good schema = organized data + faster queries [OK]

Common Mistakes:

• Thinking MongoDB enforces strict schemas like SQL
• Assuming indexes are automatic for all fields
• Believing schema design prevents all duplicates

2. Which of the following is the correct way to embed an address inside a user document in MongoDB?

easy

A. { name: 'Alice', address: { city: 'NY', zip: '10001' } }

B. { name: 'Alice', address: ['city', 'NY', 'zip', '10001'] }

C. { name: 'Alice', address: 'city: NY, zip: 10001' }

D. { name: 'Alice', address: [{ city: 'NY' }, { zip: '10001' }] }

Solution

1. Step 1: Understand embedding in MongoDB

   Embedding means putting related data as a nested object inside the main document.

2. Step 2: Check the syntax for embedded documents

   { name: 'Alice', address: { city: 'NY', zip: '10001' } } shows a nested object with keys and values inside the user document, which is correct.

3. Final Answer:

   { name: 'Alice', address: { city: 'NY', zip: '10001' } } -> Option A

4. Quick Check:

   Embedding = nested object inside document [OK]

Hint: Embed related data as nested objects, not arrays or strings [OK]

Common Mistakes:

• Using arrays instead of objects for embedded data
• Storing embedded data as plain strings
• Splitting embedded fields into separate objects in arrays

3. Given this MongoDB document structure for a blog post:

{ title: 'Post', comments: [{ user: 'Bob', text: 'Nice!' }, { user: 'Ann', text: 'Great!' }] }

What will be the result of the query db.posts.find({ 'comments.user': 'Bob' })?

medium

A. Returns an error because nested arrays can't be queried.

B. Returns posts with comments only by user 'Bob'.

C. Returns posts with no comments.

D. Returns posts that have at least one comment by user 'Bob'.

Solution

1. Step 1: Understand the query condition

   The query looks for documents where any comment's user field equals 'Bob'.

2. Step 2: Interpret MongoDB array field query

   MongoDB matches if at least one element in the array meets the condition, so it returns posts with any comment by 'Bob'.

3. Final Answer:

   Returns posts that have at least one comment by user 'Bob'. -> Option D

4. Quick Check:

   Query on array field = match any element [OK]

Hint: Query on array fields matches any element by default [OK]

Common Mistakes:

• Thinking query matches only if all comments are by 'Bob'
• Expecting error on querying nested arrays
• Assuming posts without comments are returned

4. You designed a MongoDB schema embedding orders inside a user document like this:

{ name: 'John', orders: [{ id: 1, total: 50 }, { id: 2, total: 30 }] }

But your query db.users.find({ 'orders.id': 2 }) returns no results. What is the likely problem?

medium

A. The field name 'orders.id' is incorrect; it should be 'orders._id'.

B. MongoDB cannot query inside embedded arrays.

C. The orders array is empty or the field 'id' does not exist in orders.

D. The query syntax is invalid and causes an error.

Solution

1. Step 1: Check the query and data fields

   The query looks for orders with id 2 inside the orders array.

2. Step 2: Identify possible data mismatch

   If no results, likely the orders array is empty or the 'id' field is missing or named differently.

3. Final Answer:

   The orders array is empty or the field 'id' does not exist in orders. -> Option C

4. Quick Check:

   Query no result = data missing or field name mismatch [OK]

Hint: Check data fields and array contents if query returns no results [OK]

Common Mistakes:

• Assuming MongoDB can't query embedded arrays
• Confusing field names like 'id' vs '_id'
• Believing query syntax is invalid when it is correct

5. You want to design a MongoDB schema for a social app where users have many posts and each post has many comments. Which schema design is best for fast reading of a user's posts with comments?

hard

A. Embed comments inside each post, and embed posts inside the user document.

B. Embed posts inside users, but store comments in a separate collection with references.

C. Store all data in one big collection without nesting.

D. Store users, posts, and comments all in separate collections with references.

Solution

1. Step 1: Consider embedding posts inside users

   Embedding posts inside users helps quickly get all posts for a user in one query.

2. Step 2: Consider comments size and frequency

   Comments can be many and large, so storing them separately avoids large user documents and improves performance.

3. Step 3: Choose best balance

   Embedding posts but referencing comments balances fast user-post reads and manageable comment data.

4. Final Answer:

   Embed posts inside users, but store comments in a separate collection with references. -> Option B

5. Quick Check:

   Embed posts + ref comments = fast reads + manageable size [OK]

Hint: Embed small related data; reference large or growing data [OK]

Common Mistakes:

• Embedding all data causing huge documents
• Referencing everything causing many slow joins
• Ignoring data size and access patterns