Computed pattern for pre-aggregation in MongoDB - Mini Project: Build & Apply

Computed Pattern for Pre-Aggregation in MongoDB

📖 Scenario: You work for a small online bookstore. You want to keep track of the total number of books sold per author to quickly show popular authors without counting every sale each time.

🎯 Goal: Build a MongoDB collection that stores book sales and a pre-aggregated collection that keeps the total sales per author updated using computed fields.

📋 What You'll Learn

Create a sales collection with documents containing book, author, and copies_sold fields.

Create a variable min_copies to filter sales with at least 5 copies sold.

Write an aggregation pipeline that sums copies_sold per author only for sales meeting the min_copies threshold.

Create a pre_aggregated_sales collection that stores the total copies sold per author.

💡 Why This Matters

🌍 Real World

Pre-aggregation helps websites and apps show summary data quickly without recalculating totals every time a user requests it.

💼 Career

Database developers and data engineers often create pre-aggregated collections or tables to improve performance and user experience.

1

DATA SETUP: Create the sales collection with sample sales data

Create a MongoDB collection called sales and insert these exact documents: { book: "Book A", author: "Author X", copies_sold: 10 }, { book: "Book B", author: "Author Y", copies_sold: 3 }, { book: "Book C", author: "Author X", copies_sold: 7 }, { book: "Book D", author: "Author Z", copies_sold: 2 }.

MongoDB

// Insert the sales documents into the sales collection
// Your code here

Need a hint?

Use db.sales.insertMany() with an array of documents.

2

CONFIGURATION: Define a variable min_copies to filter sales

Create a variable called min_copies and set it to 5. This will be used to filter sales with at least 5 copies sold.

MongoDB

db.sales.insertMany([
  { book: "Book A", author: "Author X", copies_sold: 10 },
  { book: "Book B", author: "Author Y", copies_sold: 3 },
  { book: "Book C", author: "Author X", copies_sold: 7 },
  { book: "Book D", author: "Author Z", copies_sold: 2 }
])

// Define min_copies variable
// Your code here

Need a hint?

Use const min_copies = 5 to create the variable.

3

CORE LOGIC: Aggregate total copies sold per author with filtering

Write an aggregation pipeline on sales that filters documents where copies_sold is greater than or equal to min_copies, then groups by author and sums copies_sold as total_copies. Store the result in a variable called authorSales.

MongoDB

db.sales.insertMany([
  { book: "Book A", author: "Author X", copies_sold: 10 },
  { book: "Book B", author: "Author Y", copies_sold: 3 },
  { book: "Book C", author: "Author X", copies_sold: 7 },
  { book: "Book D", author: "Author Z", copies_sold: 2 }
])

const min_copies = 5

// Write aggregation pipeline to compute total copies sold per author
// Your code here

Need a hint?

Use $match to filter and $group to sum copies sold per author.

4

COMPLETION: Store the pre-aggregated results in pre_aggregated_sales collection

Insert the documents from authorSales into a new collection called pre_aggregated_sales.

MongoDB

db.sales.insertMany([
  { book: "Book A", author: "Author X", copies_sold: 10 },
  { book: "Book B", author: "Author Y", copies_sold: 3 },
  { book: "Book C", author: "Author X", copies_sold: 7 },
  { book: "Book D", author: "Author Z", copies_sold: 2 }
])

const min_copies = 5

const authorSales = db.sales.aggregate([
  { $match: { copies_sold: { $gte: min_copies } } },
  { $group: { _id: "$author", total_copies: { $sum: "$copies_sold" } } }
]).toArray()

// Insert authorSales into pre_aggregated_sales collection
// Your code here

Need a hint?

Use db.pre_aggregated_sales.insertMany(authorSales) to save the results.