Introduction to aggregation

An aggregation pipeline passes documents through ordered stages. Each stage works on the output of the preceding stage, which can have a different shape from the original document.

Aggregation in MongoDB is a way of processing a large number of documents and transforming them into meaningful summarized results. It is equivalent to the GROUP BY operations in SQL, but with far more flexibility and power.

The Aggregation Framework uses a concept called the aggregation pipeline, which consists of multiple stages. Each stage performs an operation on the input documents and passes the result to the next stage.

Before working through the examples, make sure you can connect with mongosh and select your database. Use a practice database for writes and deletions.

Pen: 2 units
Pen: 3 units
Book: 1 unit

{ $group: {
  _id: "$item",
  units: { $sum: "$quantity" }
} }

Pen: 5 units
Book: 1 unit

An aggregation pipeline is an array of stages. Each stage transforms the data as it passes through.

Common stages include:

• $match – Filters documents (like find())
• $group – Groups documents and performs aggregations like sum, avg
• $project – Reshapes documents (e.g., show or hide fields)
• $sort – Sorts the documents
• $limit – Limits the number of documents

Why use Aggregation?

Aggregation is used to:

• Compute totals, averages, and counts
• Group documents by a field
• Transform documents into new shapes

Example Dataset

Let’s say we have a sales collection with the following documents:

    db.sales.insertMany([
      { item: "Laptop", price: 70000, quantity: 2, region: "North" },
      { item: "Monitor", price: 12000, quantity: 5, region: "South" },
      { item: "Laptop", price: 70000, quantity: 1, region: "South" },
      { item: "Mouse", price: 500, quantity: 10, region: "North" },
      { item: "Keyboard", price: 1500, quantity: 3, region: "West" }
    ])

We want to group documents by item and calculate the total sales amount (price × quantity).

    db.sales.aggregate([
      {
        $project: {
          item: 1,
          totalSale: { $multiply: ["$price", "$quantity"] }
        }
      },
      {
        $group: {
          _id: "$item",
          totalRevenue: { $sum: "$totalSale" }
        }
      }
    ])

    { _id: "Laptop", totalRevenue: 210000 }
    { _id: "Monitor", totalRevenue: 60000 }
    { _id: "Mouse", totalRevenue: 5000 }
    { _id: "Keyboard", totalRevenue: 4500 }

Explanation:

• $project stage creates a new field totalSale by multiplying price and quantity.
• $group then aggregates these totalSale values grouped by item.

Intuition Check

Q: Why didn’t we calculate price * quantity directly inside $group?

A: Because $group cannot access multiple fields for direct arithmetic. We first need to use $project to calculate it, then use $group to aggregate the result.

    db.sales.aggregate([
      {
        $group: {
          _id: "$region",
          totalUnits: { $sum: "$quantity" }
        }
      }
    ])

    { _id: "North", totalUnits: 12 }
    { _id: "South", totalUnits: 6 }
    { _id: "West", totalUnits: 3 }

Explanation: We directly group by the region field and sum the quantity field to get total units sold per region.

Example 3: Show Only Item and Region

Let’s say we want to only view item and region, hiding everything else:

    db.sales.aggregate([
      {
        $project: {
          _id: 0,
          item: 1,
          region: 1
        }
      }
    ])

    { item: "Laptop", region: "North" }
    { item: "Monitor", region: "South" }
    { item: "Laptop", region: "South" }
    { item: "Mouse", region: "North" }
    { item: "Keyboard", region: "West" }

Explanation: The $project stage is used to control which fields are shown. Setting _id: 0 hides the default _id field.

An aggregation pipeline passes documents through ordered stages. Each stage works on the output of the preceding stage, which can have a different shape from the original document.

Key Takeaways

• Aggregation in MongoDB is done through pipelines made of stages.
• $project is used to compute fields or reshape documents.
• $group is used to aggregate data like sum, avg, count, etc.

• Review document filters