One-to-many embedding pattern in MongoDB - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the one-to-many embedding pattern in MongoDB?

It is a way to store related data by putting many related items inside a single document as an array. This keeps related data together for easy access.

beginner

Why use embedding for one-to-many relationships in MongoDB?

Embedding helps to read related data quickly in one query because all related items are inside one document. It is good when the 'many' side is not too large.

beginner

Give an example of a one-to-many embedding pattern in MongoDB.

A blog post document with an array of comments inside it. Each comment is stored as an embedded document in the comments array.

intermediate

What is a limitation of the one-to-many embedding pattern?

If the 'many' side grows too large, the document can become too big and slow to work with. MongoDB has a 16MB document size limit.

intermediate

How does one-to-many embedding differ from referencing in MongoDB?

Embedding stores related data inside one document. Referencing stores related data in separate documents and links them by IDs.

What does the one-to-many embedding pattern store inside a MongoDB document?

A Separate documents linked by IDs

B Only one related document per document

C An array of related documents inside one document

D Data in multiple collections

The one-to-many embedding pattern stores many related items as an array inside a single document.

When is embedding a good choice for one-to-many relationships?

A When the 'many' side is small or bounded

B When data must be stored in separate collections

C When the 'many' side is very large

D When you want to avoid arrays

Embedding works well when the 'many' side is small or has a known limit to keep documents manageable.

What is a risk of embedding too many related items in one document?

A It slows down network connections

B Data becomes harder to query

C Data is stored in multiple collections

D Document size may exceed MongoDB limits

Embedding too many items can make the document too large, exceeding MongoDB's 16MB document size limit.

How does embedding affect read operations?

A Requires multiple queries to get related data

B Allows fetching related data in one query

C Makes reads slower always

D Prevents indexing

Embedding allows fetching all related data in a single query, making reads faster.

Which of these is NOT true about one-to-many embedding?

A It is good for large, unbounded related data

B It has a document size limit

C It can improve read performance

D It stores related data inside one document

Embedding is not good for large, unbounded related data because of document size limits.

Explain the one-to-many embedding pattern in MongoDB and when you would use it.

Think about how related data is grouped together inside one document.

Describe the main difference between embedding and referencing for one-to-many relationships in MongoDB.

Focus on where the related data lives and how it is connected.

Practice

1. What is the main advantage of using the one-to-many embedding pattern in MongoDB?

easy

A. It separates related data into different collections for better normalization.

B. It duplicates data across multiple documents to improve write speed.

C. It requires complex joins to retrieve related data.

D. It stores related data inside the main document for faster reads.

Solution

1. Step 1: Understand embedding in MongoDB

   Embedding means storing related data inside the main document instead of separate collections.

2. Step 2: Identify the advantage of embedding

   This approach makes reading related data faster and simpler because all data is in one place.

3. Final Answer:

   It stores related data inside the main document for faster reads. -> Option D

4. Quick Check:

   Embedding = faster reads [OK]

Hint: Embedding means related data inside main document for quick access [OK]

Common Mistakes:

• Confusing embedding with referencing (separate collections)
• Thinking embedding requires joins
• Assuming embedding duplicates data

2. Which of the following is the correct way to embed multiple comments inside a blog post document in MongoDB?

easy

A. { _id: 1, title: 'Post', comments: 'Alice: Nice!, Bob: Great!' }

B. { _id: 1, title: 'Post', comments: { user: 'Alice', text: 'Nice!' }, { user: 'Bob', text: 'Great!' } }

C. { _id: 1, title: 'Post', comments: [{ user: 'Alice', text: 'Nice!' }, { user: 'Bob', text: 'Great!' }] }

D. { _id: 1, title: 'Post', comments: [{ user: 'Alice' }, user: 'Bob', text: 'Great!' ] }

Solution

1. Step 1: Check the structure for embedding multiple comments

   Embedding many comments requires an array of objects inside the main document.

2. Step 2: Identify the correct JSON syntax

   { _id: 1, title: 'Post', comments: [{ user: 'Alice', text: 'Nice!' }, { user: 'Bob', text: 'Great!' }] } correctly uses an array of comment objects inside the comments field.

3. Final Answer:

   { _id: 1, title: 'Post', comments: [{ user: 'Alice', text: 'Nice!' }, { user: 'Bob', text: 'Great!' }] } -> Option C

4. Quick Check:

   Array of objects = correct embedding [OK]

Hint: Embed many items as an array of objects inside the document [OK]

Common Mistakes:

• Using multiple objects without array brackets
• Storing comments as a string instead of objects
• Incorrect JSON syntax with misplaced commas or braces

3. Given the following document in a posts collection:

{ _id: 1, title: 'Hello', comments: [{ user: 'Alice', text: 'Hi' }, { user: 'Bob', text: 'Hello' }] }

What will be the result of the query db.posts.find({ _id: 1 }, { 'comments.user': 1, _id: 0 })?

medium

A. { comments: [{ user: 'Alice' }, { user: 'Bob' }] }

B. { comments: [{ text: 'Hi' }, { text: 'Hello' }] }

C. { _id: 1, comments: [{ user: 'Alice' }, { user: 'Bob' }] }

D. { comments: ['Alice', 'Bob'] }

Solution

1. Step 1: Understand the projection in the query

   The query projects only the comments.user field and excludes _id.

2. Step 2: Determine the output structure

   It returns the comments array with only the user fields inside each comment object.

3. Final Answer:

   { comments: [{ user: 'Alice' }, { user: 'Bob' }] } -> Option A

4. Quick Check:

   Projection of comments.user = array of user objects [OK]

Hint: Projection with dot notation returns array of objects with that field [OK]

Common Mistakes:

• Expecting full comment objects instead of only user fields
• Including _id when excluded in projection
• Getting array of strings instead of objects

4. You have this document:

{ _id: 1, name: 'Store', products: [{ name: 'Pen', price: 1.5 }, { name: 'Pencil', price: 1.0 }] }

Which update query correctly adds a new product { name: 'Eraser', price: 0.5 } to the products array?

medium

A. db.stores.updateOne({ _id: 1 }, { $push: { products: { name: 'Eraser', price: 0.5 } } })

B. db.stores.updateOne({ _id: 1 }, { $addToSet: { products: 'Eraser' } })

C. db.stores.updateOne({ _id: 1 }, { $set: { products: { name: 'Eraser', price: 0.5 } } })

D. db.stores.updateOne({ _id: 1 }, { $push: { products: ['Eraser', 0.5] } })

Solution

1. Step 1: Identify the correct operator to add an object to an array

   The $push operator adds an element to an array field.

2. Step 2: Check the value being pushed

   db.stores.updateOne({ _id: 1 }, { $push: { products: { name: 'Eraser', price: 0.5 } } }) pushes the correct object { name: 'Eraser', price: 0.5 } into the products array.

3. Final Answer:

   db.stores.updateOne({ _id: 1 }, { $push: { products: { name: 'Eraser', price: 0.5 } } }) -> Option A

4. Quick Check:

   $push with object adds to array [OK]

Hint: Use $push with full object to add to embedded array [OK]

Common Mistakes:

• Using $set replaces whole array instead of adding
• Using $addToSet with a string instead of object
• Pushing array instead of object inside array

5. You want to store a user's orders in a single document using one-to-many embedding. Each order has an orderId, date, and a list of items. Which schema design is best to handle up to 100 orders per user efficiently?

hard

A. Separate orders collection with userId reference for each order

B. { _id: userId, name: 'User', orders: [{ orderId, date, items: [{ productId, qty }] }] }

C. { _id: userId, name: 'User', orderIds: [orderId1, orderId2], orders: [] }

D. { _id: userId, name: 'User', orders: { orderId, date, items: [{ productId, qty }] } }

Solution

1. Step 1: Understand embedding for one-to-many with moderate size

   Embedding up to 100 orders inside a user document is reasonable for fast reads.

2. Step 2: Check schema design for correct embedding

   { _id: userId, name: 'User', orders: [{ orderId, date, items: [{ productId, qty }] }] } embeds an array of order objects, each with its own items array, which fits the one-to-many embedding pattern well.

3. Final Answer:

   { _id: userId, name: 'User', orders: [{ orderId, date, items: [{ productId, qty }] }] } -> Option B

4. Quick Check:

   Embed array of orders with nested items for one-to-many [OK]

Hint: Embed arrays of objects for moderate related data size [OK]

Common Mistakes:

• Using object instead of array for multiple orders
• Separating orders into another collection unnecessarily
• Storing only order IDs without details inside user