Denormalization trade-offs in MongoDB - Mini Project: Build & Apply

Denormalization Trade-offs in MongoDB

📖 Scenario: You are building a simple online bookstore database using MongoDB. You want to understand how denormalization works by embedding author details directly inside book documents instead of referencing them separately.

🎯 Goal: Create a MongoDB collection with books that embed author information. Then add a configuration variable to control whether to embed or reference authors. Finally, write a query to retrieve books with embedded author details.

📋 What You'll Learn

Create a books collection with embedded author details

Add a variable embedAuthors to control embedding

Write a query to find all books with embedded author names

Add a final step to show how to switch between embedding and referencing

💡 Why This Matters

🌍 Real World

Denormalization by embedding related data is common in MongoDB to improve read performance by reducing the need for joins.

💼 Career

Understanding when to embed or reference data is key for designing efficient NoSQL databases in real-world applications.

1

Create the books collection with embedded authors

Create a MongoDB collection called books with two documents. Each document must have a title field and an embedded author field. The author field should be an object with name and birthYear. Use these exact values: First book: title "The Great Gatsby", author name "F. Scott Fitzgerald", birthYear 1896. Second book: title "1984", author name "George Orwell", birthYear 1903.

MongoDB

// Your code here to create the books collection with embedded authors

Hint

Use db.books.insertMany() with an array of objects. Each object should have title and author fields. The author is an embedded object.

2

Add a variable embedAuthors to control embedding

Create a JavaScript variable called embedAuthors and set it to true. This variable will control if authors are embedded or referenced.

MongoDB

db.books.insertMany([
  {
    title: "The Great Gatsby",
    author: { name: "F. Scott Fitzgerald", birthYear: 1896 }
  },
  {
    title: "1984",
    author: { name: "George Orwell", birthYear: 1903 }
  }
])

// Your code here to add embedAuthors variable

Hint

Use const embedAuthors = true to create the variable.

3

Write a query to find all books with embedded author names

Write a MongoDB query to find all documents in the books collection where the author.name field exists. Use db.books.find() with a filter that checks for author.name.

MongoDB

db.books.insertMany([
  {
    title: "The Great Gatsby",
    author: { name: "F. Scott Fitzgerald", birthYear: 1896 }
  },
  {
    title: "1984",
    author: { name: "George Orwell", birthYear: 1903 }
  }
])

const embedAuthors = true;

// Your code here to query books with embedded author names

Hint

Use db.books.find({ "author.name": { $exists: true } }) to find books with embedded author names.

4

Add a final step to show switching between embedding and referencing

Add an if statement that checks if embedAuthors is true. If true, assign the embedded author object to a variable called bookAuthor. Otherwise, assign a reference ID string "authorId123" to bookAuthor.

MongoDB

db.books.insertMany([
  {
    title: "The Great Gatsby",
    author: { name: "F. Scott Fitzgerald", birthYear: 1896 }
  },
  {
    title: "1984",
    author: { name: "George Orwell", birthYear: 1903 }
  }
])

const embedAuthors = true;

db.books.find({ "author.name": { $exists: true } })

// Your code here to add if statement for embedding or referencing

Hint

Use if (embedAuthors) { bookAuthor = {...} } else { bookAuthor = "authorId123" } to show the trade-off.

Practice

1. What is a main advantage of denormalization in MongoDB?

easy

A. Faster read operations by storing related data together

B. Simpler updates with no data duplication

C. Reduced storage space by avoiding repeated data

D. Automatic data consistency without extra effort

Solution

1. Step 1: Understand denormalization purpose

   Denormalization stores related data together to speed up reading.

2. Step 2: Compare options with this purpose

   Only Faster read operations by storing related data together mentions faster reads by storing data together, which matches denormalization benefits.

3. Final Answer:

   Faster read operations by storing related data together -> Option A

4. Quick Check:

   Denormalization = Faster reads [OK]

Hint: Denormalization speeds up reads by grouping data [OK]

Common Mistakes:

• Confusing denormalization with normalization benefits
• Thinking denormalization reduces storage space
• Assuming denormalization simplifies updates

2. Which MongoDB document structure shows denormalization?

easy

A. { _id: 1, name: 'Alice' }, { _id: 101, total: 50 }

B. { _id: 1, name: 'Alice', orders: [{id: 101, total: 50}] }

C. { _id: 1, name: 'Alice' }, orders: 101

D. { _id: 1, orders: [101] }

Solution

1. Step 1: Identify denormalized data structure

   Denormalization stores related data inside one document, like embedding orders inside a user document.

2. Step 2: Check options for embedded data

   { _id: 1, name: 'Alice', orders: [{id: 101, total: 50}] } embeds orders array inside user document, showing denormalization.

3. Final Answer:

   { _id: 1, name: 'Alice', orders: [{id: 101, total: 50}] } -> Option B

4. Quick Check:

   Embedded orders = Denormalization [OK]

Hint: Look for embedded arrays inside documents [OK]

Common Mistakes:

• Choosing separate documents as denormalized
• Confusing references with embedded data
• Ignoring array embedding as denormalization

3. Given this denormalized document:

{ _id: 1, name: 'Bob', address: { city: 'NY', zip: '10001' } }

What happens if Bob moves to 'LA' and you update only the city in this document?

medium

A. Update fails due to embedded document structure

B. All references to Bob's city update automatically

C. Only this document's city changes; duplicates elsewhere stay old

D. MongoDB merges old and new city values

Solution

1. Step 1: Understand update impact in denormalization

   Denormalization duplicates data, so updating one copy doesn't affect others.

2. Step 2: Analyze update effect on embedded address

   Updating city in one document changes only that document; duplicates elsewhere remain unchanged.

3. Final Answer:

   Only this document's city changes; duplicates elsewhere stay old -> Option C

4. Quick Check:

   Update one copy, duplicates unchanged [OK]

Hint: Updates affect only one copy in denormalized data [OK]

Common Mistakes:

• Assuming all duplicates update automatically
• Thinking embedded documents prevent updates
• Believing MongoDB merges conflicting values

4. You have duplicated user phone numbers in multiple documents due to denormalization. Which is a common problem you might face?

medium

A. Updating phone number requires changing all documents manually

B. MongoDB automatically syncs all duplicates on update

C. Phone numbers cannot be stored in embedded documents

D. Denormalization prevents querying phone numbers

Solution

1. Step 1: Identify update challenges with duplicated data

   Duplicated data means updates must be applied to every copy to keep consistency.

2. Step 2: Evaluate options about update behavior

   Only Updating phone number requires changing all documents manually correctly states that manual updates to all duplicates are needed.

3. Final Answer:

   Updating phone number requires changing all documents manually -> Option A

4. Quick Check:

   Manual update needed for duplicates [OK]

Hint: Duplicates need manual updates to stay consistent [OK]

Common Mistakes:

• Believing MongoDB auto-syncs duplicates
• Thinking denormalization forbids embedded fields
• Assuming denormalization blocks queries

5. You want to design a MongoDB schema for a blog where posts have many comments. You expect many reads but few comment edits. Which denormalization approach is best?

hard

A. Duplicate comments in both posts and a separate collection

B. Store comments in a separate collection and reference post IDs

C. Store only comment IDs inside posts without comment data

D. Embed all comments inside each post document

Solution

1. Step 1: Analyze read vs update frequency

   Many reads and few updates favor denormalization to speed up reads.

2. Step 2: Choose schema matching denormalization benefits

   Embedding comments inside posts (Embed all comments inside each post document) stores related data together for fast reads.

3. Step 3: Consider update cost

   Few comment edits mean duplicated data update cost is low, so embedding is suitable.

4. Final Answer:

   Embed all comments inside each post document -> Option D

5. Quick Check:

   Many reads + few updates = Embed data [OK]

Hint: Embed when reads >> updates for faster access [OK]

Common Mistakes:

• Choosing references despite many reads
• Duplicating data unnecessarily increasing update cost
• Ignoring update frequency in design