Atlas search overview in MongoDB - Mini Project: Build & Apply

Atlas Search Overview

📖 Scenario: You are building a simple product catalog search feature for an online store using MongoDB Atlas Search. This will help users find products by name and description quickly.

🎯 Goal: Create a MongoDB collection with product data, configure an Atlas Search index, write a search query using the $search stage, and retrieve matching products.

📋 What You'll Learn

Create a MongoDB collection named products with sample product documents

Add a configuration variable for the search index name

Write an aggregation query using the $search stage with the index name

Complete the query to return product names and descriptions matching a search term

💡 Why This Matters

🌍 Real World

Atlas Search helps build fast and relevant search features for applications like online stores, catalogs, and content libraries.

💼 Career

Understanding Atlas Search is valuable for roles involving MongoDB database management, backend development, and building search-driven user experiences.

1

DATA SETUP: Create the products collection with sample data

Create a MongoDB collection called products and insert these exact documents: { _id: 1, name: "Red T-Shirt", description: "A bright red t-shirt made of cotton" }, { _id: 2, name: "Blue Jeans", description: "Comfortable blue denim jeans" }, and { _id: 3, name: "Green Hat", description: "A stylish green hat for sunny days" }.

MongoDB

// Insert the three product documents into the products collection
// Your code here

Need a hint?

Use db.products.insertMany([...]) with the exact documents listed.

2

CONFIGURATION: Define the search index name

Create a variable called searchIndex and set it to the string "default", which is the name of the Atlas Search index you will use.

MongoDB

db.products.insertMany([
  { _id: 1, name: "Red T-Shirt", description: "A bright red t-shirt made of cotton" },
  { _id: 2, name: "Blue Jeans", description: "Comfortable blue denim jeans" },
  { _id: 3, name: "Green Hat", description: "A stylish green hat for sunny days" }
])

// Create a variable called searchIndex and set it to "default"
// Your code here

Need a hint?

Use const searchIndex = "default" to define the index name.

3

CORE LOGIC: Write the Atlas Search aggregation query

Write an aggregation query on the products collection using db.products.aggregate(). Use the $search stage with index: searchIndex and a text operator to search the name and description fields for the term "red". Store the query in a variable called searchQuery.

MongoDB

db.products.insertMany([
  { _id: 1, name: "Red T-Shirt", description: "A bright red t-shirt made of cotton" },
  { _id: 2, name: "Blue Jeans", description: "Comfortable blue denim jeans" },
  { _id: 3, name: "Green Hat", description: "A stylish green hat for sunny days" }
])

const searchIndex = "default"

// Write an aggregation query using $search with index searchIndex to find "red" in name and description
// Store the query in searchQuery
// Your code here

Need a hint?

Use db.products.aggregate([{ $search: { index: searchIndex, text: { query: "red", path: ["name", "description"] } } }]) and assign it to searchQuery.

4

COMPLETION: Add a projection stage to return only name and description

Add a $project stage to the searchQuery aggregation pipeline to return only the name and description fields. Update the searchQuery variable to include this stage.

MongoDB

db.products.insertMany([
  { _id: 1, name: "Red T-Shirt", description: "A bright red t-shirt made of cotton" },
  { _id: 2, name: "Blue Jeans", description: "Comfortable blue denim jeans" },
  { _id: 3, name: "Green Hat", description: "A stylish green hat for sunny days" }
])

const searchIndex = "default"

const searchQuery = db.products.aggregate([
  {
    $search: {
      index: searchIndex,
      text: {
        query: "red",
        path: ["name", "description"]
      }
    }
  },
  // Add a $project stage here to return only name and description
  // Your code here
])

Need a hint?

Add { $project: { _id: 0, name: 1, description: 1 } } as the second stage in the aggregation pipeline.