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

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

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

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

Hint

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

Practice

1. What is the main purpose of using a computed pattern for pre-aggregation in MongoDB?

easy

A. To delete all raw data after aggregation

B. To create indexes on every field automatically

C. To calculate and store summary data ahead of time for faster queries

D. To replicate data across multiple servers

Solution

1. Step 1: Understand pre-aggregation concept

   Pre-aggregation means computing summary data before queries run, so queries are faster.

2. Step 2: Identify the main goal

   The goal is to store computed summaries to avoid recalculating on every query.

3. Final Answer:

   To calculate and store summary data ahead of time for faster queries -> Option C

4. Quick Check:

   Pre-aggregation = faster queries [OK]

Hint: Pre-aggregation means compute summaries before queries [OK]

Common Mistakes:

• Confusing pre-aggregation with data deletion
• Thinking it automatically creates indexes
• Mixing it with data replication

2. Which MongoDB aggregation stage is primarily used to compute grouped summary data for pre-aggregation?

easy

A. $match

B. $group

C. $project

D. $sort

Solution

1. Step 1: Recall aggregation stages

   $group is used to group documents and compute summary values like sums or counts.

2. Step 2: Identify correct stage for pre-aggregation

   Pre-aggregation requires grouping and computing, so $group is the right stage.

3. Final Answer:

   $group -> Option B

4. Quick Check:

   $group = grouping and computing summaries [OK]

Hint: Use $group to compute grouped summaries [OK]

Common Mistakes:

• Choosing $match which filters but does not compute
• Confusing $project with grouping
• Selecting $sort which only orders data

3. Given the following aggregation pipeline, what will be the output?

[{ $group: { _id: "$category", total: { $sum: "$amount" } } }]

Assuming the collection has documents: {category: "A", amount: 10}, {category: "B", amount: 5}, {category: "A", amount: 15}.

medium

A. [{_id: null, total: 30}]

B. [{_id: "A", total: 10}, {_id: "B", total: 5}, {_id: "A", total: 15}]

C. [{_id: "A", total: 15}, {_id: "B", total: 5}]

D. [{_id: "A", total: 25}, {_id: "B", total: 5}]

Solution

1. Step 1: Group documents by category

   Documents with category "A" are grouped together, and category "B" separately.

2. Step 2: Sum amounts per group

   For "A": 10 + 15 = 25; for "B": 5.

3. Final Answer:

   [{_id: "A", total: 25}, {_id: "B", total: 5}] -> Option D

4. Quick Check:

   Sum amounts by category = correct totals [OK]

Hint: Sum amounts grouped by _id field [OK]

Common Mistakes:

• Not grouping sums correctly
• Returning individual documents instead of grouped
• Using null _id which sums all documents

4. Identify the error in this aggregation pipeline for pre-aggregation:

[{ $group: { _id: "$category", total: { $sum: "amount" } } }]

medium

A. Missing $ before amount in $sum operator

B. Incorrect use of $group stage

C. Missing $match stage before $group

D. Using _id field incorrectly

Solution

1. Step 1: Check $sum operator syntax

   $sum requires a field path starting with $, but "amount" is missing the $.

2. Step 2: Confirm correct syntax

   It should be $sum: "$amount" to sum the amount field values.

3. Final Answer:

   Missing $ before amount in $sum operator -> Option A

4. Quick Check:

   $sum needs $field syntax [OK]

Hint: Field names in aggregation need $ prefix [OK]

Common Mistakes:

• Forgetting $ before field names in operators
• Assuming $match is mandatory before $group
• Misusing _id field in $group

5. You want to pre-aggregate sales data by month and product category, storing total sales and average price. Which aggregation pipeline correctly computes these values?

hard

A. [{ $group: { _id: { month: { $month: "$date" }, category: "$category" }, totalSales: { $sum: "$amount" }, avgPrice: { $avg: "$price" } } }]

B. [{ $group: { _id: "$date", totalSales: { $sum: "$amount" }, avgPrice: { $avg: "$price" } } }]

C. [{ $group: { _id: { month: "$date", category: "$category" }, totalSales: { $sum: "$amount" }, avgPrice: { $avg: "$price" } } }]

D. [{ $group: { _id: { month: { $month: "$date" }, category: "$category" }, totalSales: { $sum: "$price" }, avgPrice: { $avg: "$amount" } } }]

Solution

1. Step 1: Group by month and category correctly

   Use $month operator on "$date" to extract month, and include "$category" as second key.

2. Step 2: Compute total sales and average price correctly

   Sum the "$amount" field for totalSales and average the "$price" field for avgPrice.

3. Final Answer:

   [{ $group: { _id: { month: { $month: "$date" }, category: "$category" }, totalSales: { $sum: "$amount" }, avgPrice: { $avg: "$price" } } }] -> Option A

4. Quick Check:

   Group by month+category, sum amount, avg price [OK]

Hint: Use $month for date grouping and correct fields for sum/avg [OK]

Common Mistakes:

• Grouping by full date instead of month
• Swapping sum and avg fields
• Not using $month operator for month extraction