Why document databases over relational in MongoDB - See It in Action

Why Document Databases Over Relational Databases

📖 Scenario: You are working for a small online bookstore. The store wants to keep track of books, authors, and customer reviews. You need to decide how to store this data efficiently.

🎯 Goal: Build a simple document database structure using MongoDB that shows why document databases can be better than relational databases for this use case.

📋 What You'll Learn

Create a collection called books with embedded author and reviews data

Add a configuration variable maxReviews to limit the number of reviews stored

Write a query to find books with more than maxReviews reviews

Add a final index on the title field to speed up searches

💡 Why This Matters

🌍 Real World

Document databases like MongoDB are great for storing complex data with nested structures, such as books with authors and reviews all in one place. This reduces the need for complex joins and makes data retrieval faster and simpler.

💼 Career

Many modern applications use document databases for flexible and scalable data storage. Knowing how to design and query these databases is valuable for backend developers, data engineers, and database administrators.

1

DATA SETUP: Create the books collection with embedded documents

Create a MongoDB collection called books with these exact documents: one book titled 'Learn MongoDB' by author { name: 'Alice', age: 30 } and two reviews { user: 'Bob', rating: 5 } and { user: 'Carol', rating: 4 } embedded inside the book document.

MongoDB

# Your code here to create the books collection with embedded author and reviews

Need a hint?

Use insertOne to add a document with embedded author and reviews arrays.

2

CONFIGURATION: Add a variable to limit reviews

Create a variable called maxReviews and set it to 1 to limit how many reviews you want to consider for filtering.

MongoDB

db.books.insertOne({
  title: 'Learn MongoDB',
  author: { name: 'Alice', age: 30 },
  reviews: [
    { user: 'Bob', rating: 5 },
    { user: 'Carol', rating: 4 }
  ]
})

// Your code here to create maxReviews variable

Need a hint?

Use const maxReviews = 1 to create the variable.

3

CORE LOGIC: Query books with more than maxReviews reviews

Write a MongoDB query using find to get all books where the number of reviews is greater than maxReviews. Use $expr and $gt operators with $size on the reviews array.

MongoDB

db.books.insertOne({
  title: 'Learn MongoDB',
  author: { name: 'Alice', age: 30 },
  reviews: [
    { user: 'Bob', rating: 5 },
    { user: 'Carol', rating: 4 }
  ]
})

const maxReviews = 1;

// Your code here to query books with more than maxReviews reviews

Need a hint?

Use db.books.find({ $expr: { $gt: [ { $size: '$reviews' }, maxReviews ] } }) to filter.

4

COMPLETION: Add an index on the title field

Create an index on the title field in the books collection using createIndex to speed up searches by title.

MongoDB

db.books.insertOne({
  title: 'Learn MongoDB',
  author: { name: 'Alice', age: 30 },
  reviews: [
    { user: 'Bob', rating: 5 },
    { user: 'Carol', rating: 4 }
  ]
})

const maxReviews = 1;

const booksWithManyReviews = db.books.find({
  $expr: { $gt: [ { $size: '$reviews' }, maxReviews ] }
})

// Your code here to create an index on title

Need a hint?

Use db.books.createIndex({ title: 1 }) to create the index.