Using $match, $group, $project, $sort in MongoDB Aggregation

Using $match, $group, $project, $sort in MongoDB Aggregation

The MongoDB Aggregation Framework allows you to process and transform large volumes of documents in stages. Think of it as a data pipeline where each stage performs a specific task such as filtering, grouping, transforming, or sorting the data.

The most commonly used stages are:

• $match – Filter documents (like WHERE in SQL)
• $group – Group and aggregate values (like GROUP BY)
• $project – Include, exclude, or transform fields
• $sort – Sort documents in ascending or descending order

Example Dataset: Sales Orders

Let’s assume you have a collection called orders with documents like this:

    db.orders.insertMany([
      { item: "pen", category: "stationery", price: 10, quantity: 3, store: "A" },
      { item: "notebook", category: "stationery", price: 50, quantity: 2, store: "A" },
      { item: "eraser", category: "stationery", price: 5, quantity: 5, store: "B" },
      { item: "apple", category: "grocery", price: 20, quantity: 10, store: "B" },
      { item: "banana", category: "grocery", price: 15, quantity: 8, store: "A" }
    ]);

$match – Filter Documents

$match is used to filter documents based on conditions. It’s similar to WHERE in SQL.

    db.orders.aggregate([
      { $match: { category: "stationery" } }
    ]);

    [
      { item: "pen", category: "stationery", price: 10, quantity: 3, store: "A" },
      { item: "notebook", category: "stationery", price: 50, quantity: 2, store: "A" },
      { item: "eraser", category: "stationery", price: 5, quantity: 5, store: "B" }
    ]

Explanation: This filters only the documents where category is "stationery".

Question:

Can you combine multiple conditions in $match like price > 10 and category = "stationery"?

Answer: Yes, by using operators like $gt, $and, etc.

    db.orders.aggregate([
      { $match: { category: "stationery", price: { $gt: 10 } } }
    ]);

$group – Aggregate Data

$group helps us to group documents and perform operations like sum, avg, count, etc.

Example: Calculate total quantity sold per category

    db.orders.aggregate([
      { $group: { _id: "$category", totalQuantity: { $sum: "$quantity" } } }
    ]);

    [
      { _id: "stationery", totalQuantity: 10 },
      { _id: "grocery", totalQuantity: 18 }
    ]

Explanation: We grouped by category and summed the quantity field for each category.

Question:

What if you want to group by store and get the total revenue (price × quantity) from each store?

Answer: You’ll need to first calculate revenue, and then group. Let’s use $project to create a new field.

$project – Transform Fields

$project is used to reshape documents — you can include/exclude fields or create new ones.

Example: Create a new field revenue = price * quantity

    db.orders.aggregate([
      {
        $project: {
          item: 1,
          store: 1,
          revenue: { $multiply: ["$price", "$quantity"] }
        }
      }
    ]);

    [
      { item: "pen", store: "A", revenue: 30 },
      { item: "notebook", store: "A", revenue: 100 },
      { item: "eraser", store: "B", revenue: 25 },
      { item: "apple", store: "B", revenue: 200 },
      { item: "banana", store: "A", revenue: 120 }
    ]

Explanation: $multiply is used to calculate revenue by multiplying price and quantity.

Now combine $project and $group

Let’s find total revenue per store.

    db.orders.aggregate([
      {
        $project: {
          store: 1,
          revenue: { $multiply: ["$price", "$quantity"] }
        }
      },
      {
        $group: {
          _id: "$store",
          totalRevenue: { $sum: "$revenue" }
        }
      }
    ]);

    [
      { _id: "A", totalRevenue: 250 },
      { _id: "B", totalRevenue: 225 }
    ]

$sort – Sorting Results

Use $sort to arrange documents in ascending (1) or descending (-1) order.

Example: Sort stores by total revenue (highest first)

    db.orders.aggregate([
      {
        $project: {
          store: 1,
          revenue: { $multiply: ["$price", "$quantity"] }
        }
      },
      {
        $group: {
          _id: "$store",
          totalRevenue: { $sum: "$revenue" }
        }
      },
      { $sort: { totalRevenue: -1 } }
    ]);

    [
      { _id: "A", totalRevenue: 250 },
      { _id: "B", totalRevenue: 225 }
    ]

Summary

• $match filters documents
• $project transforms or adds new fields
• $group aggregates data
• $sort arranges data in a specific order

These four stages form the backbone of MongoDB's data processing capabilities. Practice chaining them to build powerful queries for real-world use cases.

Next: Dive deeper into aggregation pipelines and explore advanced operators like $unwind and $lookup.