MongoDB Aggregation Roadmap – A 7-Day Study Plan

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 le…


This content originally appeared on DEV Community and was authored by Rayan Hossain

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" } }
]);

Show only name & email:

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

Find users younger than 25:

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

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 } }
]);

Get 2 youngest users:

db.users.aggregate([
  { $sort: { age: 1 } }, 
  { $limit: 2 }
]);

Skip first order, show next 2:

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

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 } } }
]);

Average price per category:

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

Total revenue:

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

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"] } 
  }}
]);

Unwind product tags:

db.products.aggregate([
  { $unwind: "$tags" }
]);

Count number of tags per product:

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

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" 
  }}
]);

Join products with reviews:

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

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" 
  }}
]);

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"
    }
  }
]);

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"
    }
  }
]);

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" }
]

📁 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"] }
]

📁 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" } }
]

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

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

Happy Learning! 🚀


This content originally appeared on DEV Community and was authored by Rayan Hossain


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