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Rayan Hossain
Rayan Hossain

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MongoDB Aggregation Roadmap – A 7-Day Study Plan

#mongodb #tutorial #learning #webdev

MongoDB’s Aggregation Framework is more than just querying data — it’s about analyzing, transforming, and connecting collections to unlock powerful insights.

This 7-day study guide is designed to take you from beginner to confident user, with daily lessons, examples, practice problems, and projects.

Day 1 – $match & $project

Examples:

Find all Bangladeshi users:

db.users.aggregate([ { $match: { country: "Bangladesh" } } ]);
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Show only name & email:

db.users.aggregate([ { $project: { _id: 0, fullName: { $concat: ["$firstName", " ", "$lastName"] }, email: 1 }} ]);
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Find users younger than 25:

db.users.aggregate([ { $match: { age: { $lt: 25 } } } ]);
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Practice Questions:

  1. Find all users from the USA.
  2. Show only firstName and age of all users.
  3. Find products cheaper than 100.
  4. Show only name and category for products.

Day 2 – $sort, $limit, $skip

Examples:

Sort products by price descending:

db.products.aggregate([ { $sort: { price: -1 } } ]);
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Get 2 youngest users:

db.users.aggregate([ { $sort: { age: 1 } }, { $limit: 2 } ]);
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Skip first order, show next 2:

db.orders.aggregate([ { $skip: 1 }, { $limit: 2 } ]);
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Practice Questions:

  1. Get the top 2 most expensive products.
  2. Get the oldest 1 user.
  3. Sort orders by createdAt newest first.
  4. Show 2nd and 3rd most expensive products.

Day 3 – $group

Examples:

Total orders by status:

db.orders.aggregate([ { $group: { _id: "$status", total: { $sum: 1 } } } ]);
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Average price per category:

db.products.aggregate([ { $group: { _id: "$category", avgPrice: { $avg: "$price" } } } ]);
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Total revenue:

db.orders.aggregate([ { $group: { _id: null, totalRevenue: { $sum: "$amount" } } } ]);
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Practice Questions:

  1. Count users per country.
  2. Find min & max product price.
  3. Count how many reviews each product has.
  4. Find the average age of all users.

Day 4 – $addFields & $unwind

Examples:

Add fullName field:

db.users.aggregate([ { $addFields: { fullName: { $concat: ["$firstName", " ", "$lastName"] } }} ]);
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Unwind product tags:

db.products.aggregate([ { $unwind: "$tags" } ]);
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Count number of tags per product:

db.products.aggregate([ { $project: { name: 1, tagCount: { $size: "$tags" } } } ]);
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Practice Questions:

  1. Add a field isAdult = true if age >= 18.
  2. Unwind orders by productId.
  3. Count how many tags contain the word "tech".
  4. Add field priceWithTax = price * 1.15.

Day 5 – $lookup

Examples:

Join users with orders:

db.users.aggregate([ { $lookup: { from: "orders", localField: "_id", foreignField: "userId", as: "userOrders" }} ]);
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Join products with reviews:

db.products.aggregate([ { $lookup: { from: "reviews", localField: "_id", foreignField: "productId", as: "productReviews" }} ]);
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Users with their orders and products:

db.orders.aggregate([ { $lookup: { from: "users", localField: "userId", foreignField: "_id", as: "user" }}, { $lookup: { from: "products", localField: "productId", foreignField: "_id", as: "product" }} ]);
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Practice Questions:

  1. Join orders with products to show product details.
  2. Join reviews with users to show reviewer info.
  3. Join users → orders → products (nested join).
  4. Find users with at least 1 shipped order.

Day 6 – $lookup with Pipelines

Examples:

Only shipped orders per user:

db.users.aggregate([ { $lookup: { from: "orders", let: { uid: "$_id" }, pipeline: [ { $match: { $expr: { $and: [ { $eq: ["$userId", "$$uid"] }, { $eq: ["$status", "shipped"] } ] } }} ], as: "shippedOrders" } } ]);
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Latest order per user:

db.users.aggregate([ { $lookup: { from: "orders", let: { uid: "$_id" }, pipeline: [ { $match: { $expr: { $eq: ["$userId", "$$uid"] } } }, { $sort: { createdAt: -1 } }, { $limit: 1 } ], as: "latestOrder" } } ]);
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Practice Questions:

  1. Get only reviews with a rating >= 4 for each product.
  2. For each user, get only pending orders.
  3. For each product, get the latest review.
  4. For each user, get orders in the last 7 days.

Day 7 – Real Projects

Mini Project 1 – E-commerce Dashboard

Create queries to find:

  • Total revenue
  • Top 2 categories by sales
  • Average order value
  • Number of shipped vs pending orders

Mini Project 2 – User Analytics

Create queries to find:

  • Active users per country
  • Users with more than 1 order
  • Users with average order amount > 100

Mini Project 3 – Product Insights

Create queries to find:

  • Products with the highest average rating
  • Top 2 most reviewed products
  • Products with tags "tech" and "home"

Tips for Success

  1. Practice Daily: Try to write 2-3 aggregation queries each day
  2. Start Simple: Begin with basic $match and $project, then build complexity
  3. Use MongoDB Compass: Visual query builder helps understand pipeline stages
  4. Test with Sample Data: Use MongoDB sample datasets or create your own test data
  5. Read Documentation: MongoDB's aggregation documentation is excellent
  6. Debug Step by Step: Add one stage at a time to see intermediate results

Common Operators Reference

Stage Purpose Example
$match Filter documents { $match: { age: { $gte: 18 } } }
$project Select/transform fields { $project: { name: 1, _id: 0 } }
$group Group by field(s) { $group: { _id: "$category", count: { $sum: 1 } } }
$sort Sort results { $sort: { createdAt: -1 } }
$limit Limit results { $limit: 10 }
$skip Skip documents { $skip: 5 }
$lookup Join collections { $lookup: { from: "orders", ... } }
$unwind Deconstruct arrays { $unwind: "$tags" }
$addFields Add new fields { $addFields: { total: { $add: ["$price", "$tax"] } } }

Sample Data for Practice

📁 users.json 

[ { "_id": 1, "firstName": "Rayan", "lastName": "Hossain", "age": 25, "country": "Bangladesh", "email": "rayan@example.com" }, { "_id": 2, "firstName": "Ayesha", "lastName": "Rahman", "age": 19, "country": "Bangladesh", "email": "ayesha@example.com" }, { "_id": 3, "firstName": "John", "lastName": "Smith", "age": 30, "country": "USA", "email": "john@example.com" }, { "_id": 4, "firstName": "Sara", "lastName": "Lee", "age": 22, "country": "UK", "email": "sara@example.com" } ]
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📁 products.json 

[ { "_id": 101, "name": "Smartphone", "category": "Electronics", "price": 500, "tags": ["tech", "mobile"] }, { "_id": 102, "name": "Laptop", "category": "Electronics", "price": 1200, "tags": ["tech", "computer"] }, { "_id": 103, "name": "Sofa", "category": "Furniture", "price": 700, "tags": ["home", "decor"] }, { "_id": 104, "name": "Coffee Maker", "category": "Appliances", "price": 150, "tags": ["home", "kitchen"] }, { "_id": 105, "name": "Headphones", "category": "Electronics", "price": 200, "tags": ["tech", "audio"] } ]
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📁 orders.json 

[ { "_id": 201, "userId": 1, "productId": 101, "amount": 800, "status": "shipped", "createdAt": { "$date": "2025-08-01T00:00:00Z" } }, { "_id": 202, "userId": 1, "productId": 102, "amount": 150, "status": "pending", "createdAt": { "$date": "2025-08-05T00:00:00Z" } }, { "_id": 203, "userId": 2, "productId": 103, "amount": 40, "status": "shipped", "createdAt": { "$date": "2025-08-02T00:00:00Z" } }, { "_id": 204, "userId": 3, "productId": 104, "amount": 20, "status": "shipped", "createdAt": { "$date": "2025-08-03T00:00:00Z" } } ]
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How to Import Sample Data

Using MongoDB Shell:

mongoimport --db testdb --collection users --file users.json --jsonArray mongoimport --db testdb --collection products --file products.json --jsonArray mongoimport --db testdb --collection orders --file orders.json --jsonArray
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Using MongoDB Compass:

  1. Create a new database called testdb
  2. Create collections: users, products, orders
  3. Import JSON files into respective collections

Verify Data Import:

// Check if data is imported correctly db.users.countDocuments() // Should return 4 db.products.countDocuments() // Should return 4 db.orders.countDocuments() // Should return 4
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Happy Learning! 🚀

Top comments (1)

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prime_1 profile image
Roshan Sharma •

Nice post! Solid 7-day roadmap, makes MongoDB aggregation way easier to grasp.