Pipeline mental model (stages flow) in MongoDB - Mini Project: Build & Apply

Build a MongoDB Aggregation Pipeline

📖 Scenario: You work at a small online bookstore. You have a collection of books with details like title, author, genre, and sales. You want to analyze the data to find the total sales per genre.

🎯 Goal: Create a MongoDB aggregation pipeline that groups books by genre and calculates the total sales for each genre.

📋 What You'll Learn

Create a books collection with specific book documents

Add a variable for the minimum sales threshold

Build an aggregation pipeline that filters books with sales above the threshold

Group the filtered books by genre and sum their sales

💡 Why This Matters

🌍 Real World

Aggregation pipelines help analyze and summarize large collections of data in MongoDB, such as sales reports or user activity.

💼 Career

Understanding aggregation pipelines is essential for roles like data analyst, backend developer, or database administrator working with MongoDB.

1

DATA SETUP: Create the books collection with documents

Create a variable called books that is a list of documents with these exact entries: { title: 'The Alchemist', author: 'Paulo Coelho', genre: 'Fiction', sales: 500 }, { title: 'Clean Code', author: 'Robert C. Martin', genre: 'Programming', sales: 300 }, { title: 'The Pragmatic Programmer', author: 'Andrew Hunt', genre: 'Programming', sales: 250 }, { title: '1984', author: 'George Orwell', genre: 'Fiction', sales: 400 }, and { title: 'Deep Work', author: 'Cal Newport', genre: 'Self-help', sales: 150 }.

MongoDB

# Create the books list with the specified documents
# Your code here

Need a hint?

Use a Python list of dictionaries to represent the books collection documents.

2

CONFIGURATION: Set a sales threshold variable

Create a variable called min_sales and set it to 200 to filter books with sales greater than or equal to this value.

MongoDB

books = [
    { 'title': 'The Alchemist', 'author': 'Paulo Coelho', 'genre': 'Fiction', 'sales': 500 },
    { 'title': 'Clean Code', 'author': 'Robert C. Martin', 'genre': 'Programming', 'sales': 300 },
    { 'title': 'The Pragmatic Programmer', 'author': 'Andrew Hunt', 'genre': 'Programming', 'sales': 250 },
    { 'title': '1984', 'author': 'George Orwell', 'genre': 'Fiction', 'sales': 400 },
    { 'title': 'Deep Work', 'author': 'Cal Newport', 'genre': 'Self-help', 'sales': 150 }
]
# Create min_sales variable and set it to 200
# Your code here

Need a hint?

Just create a variable min_sales and assign the number 200.

3

CORE LOGIC: Build the aggregation pipeline stages

Create a variable called pipeline that is a list of two stages: first, a $match stage that filters books with sales greater than or equal to min_sales, and second, a $group stage that groups by genre and calculates the total sales as totalSales.

MongoDB

books = [
    { 'title': 'The Alchemist', 'author': 'Paulo Coelho', 'genre': 'Fiction', 'sales': 500 },
    { 'title': 'Clean Code', 'author': 'Robert C. Martin', 'genre': 'Programming', 'sales': 300 },
    { 'title': 'The Pragmatic Programmer', 'author': 'Andrew Hunt', 'genre': 'Programming', 'sales': 250 },
    { 'title': '1984', 'author': 'George Orwell', 'genre': 'Fiction', 'sales': 400 },
    { 'title': 'Deep Work', 'author': 'Cal Newport', 'genre': 'Self-help', 'sales': 150 }
]
min_sales = 200
# Create the pipeline list with $match and $group stages
# Your code here

Need a hint?

Use a list with two dictionaries: one for $match filtering sales, one for $group grouping by genre and summing sales.

4

COMPLETION: Prepare the final aggregation command

Create a variable called aggregation_command that is a dictionary with the key aggregate set to 'books' and the key pipeline set to the pipeline variable.

MongoDB

books = [
    { 'title': 'The Alchemist', 'author': 'Paulo Coelho', 'genre': 'Fiction', 'sales': 500 },
    { 'title': 'Clean Code', 'author': 'Robert C. Martin', 'genre': 'Programming', 'sales': 300 },
    { 'title': 'The Pragmatic Programmer', 'author': 'Andrew Hunt', 'genre': 'Programming', 'sales': 250 },
    { 'title': '1984', 'author': 'George Orwell', 'genre': 'Fiction', 'sales': 400 },
    { 'title': 'Deep Work', 'author': 'Cal Newport', 'genre': 'Self-help', 'sales': 150 }
]
min_sales = 200
pipeline = [
    { '$match': { 'sales': { '$gte': min_sales } } },
    { '$group': { '_id': '$genre', 'totalSales': { '$sum': '$sales' } } }
]
# Create aggregation_command dictionary with keys 'aggregate' and 'pipeline'
# Your code here

Need a hint?

Create a dictionary with keys aggregate and pipeline using the variable pipeline.