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Aggregation Pipeline Basics

Sometimes you need more than just finding documents – you need to analyze data, calculate totals, group by categories, or transform results. MongoDB's **aggregation pipeline** is like an assembly line that processes documents through multiple stages to produce computed results.

What is the Aggregation Pipeline?

The aggregation pipeline is a framework for data aggregation. Documents pass through multiple stages, and each stage transforms the documents. It's similar to Unix pipes (`|`) where you pass output from one command to the next.

Think of the aggregation pipeline as a factory assembly line. Raw materials (documents) go in, pass through different stations (stages), and come out as finished products (results).

Basic Aggregation Syntax
db.collection.aggregate([
  { stage1 },
  { stage2 },
  { stage3 }
])

Common Aggregation Stages

StageDescription
`$match`Filters documents (like find)
`$group`Groups documents by a key and computes aggregates
`$project`Selects/shapes fields (like projection)
`$sort`Sorts documents
`$limit`Limits number of documents
`$unwind`Deconstructs an array field

Sample Data for Examples
db.orders.insertMany([
  { product: "Laptop", category: "electronics", price: 999, quantity: 2 },
  { product: "Mouse", category: "electronics", price: 25, quantity: 5 },
  { product: "Book", category: "books", price: 15, quantity: 10 },
  { product: "Headphones", category: "electronics", price: 50, quantity: 3 },
  { product: "Desk", category: "furniture", price: 200, quantity: 1 },
  { product: "Notebook", category: "books", price: 5, quantity: 20 }
])

Example 1: $match – Filtering
<!-- Get all electronics orders -->
db.orders.aggregate([
  { $match: { category: "electronics" } }
]).pretty()

Example 2: $group – Grouping and Calculating
<!-- Calculate total sales by category -->
db.orders.aggregate([
  {
    $group: {
      _id: "$category", <!-- Group by category -->
      totalQuantity: { $sum: "$quantity" },
      totalRevenue: { $sum: { $multiply: ["$price", "$quantity"] } },
      averagePrice: { $avg: "$price" },
      count: { $sum: 1 }
    }
  }
]).pretty()

Output:
{
  "_id": "electronics",
  "totalQuantity": 10,
  "totalRevenue": 2198,
  "averagePrice": 358,
  "count": 3
}

Example 3: $project – Shaping Results
<!-- Show product name, price, and a calculated discount price -->
db.orders.aggregate([
  {
    $project: {
      product: 1,
      price: 1,
      discountPrice: { $multiply: ["$price", 0.9] },
      priceCategory: {
        $cond: { if: { $gte: ["$price", 100] }, then: "expensive", else: "cheap" }
      }
    }
  }
]).pretty()

Example 4: Multiple Stages Together
<!-- Get top 2 categories by revenue, show only category and revenue -->
db.orders.aggregate([
  {
    $group: {
      _id: "$category",
      revenue: { $sum: { $multiply: ["$price", "$quantity"] } }
    }
  },
  { $sort: { revenue: -1 } },
  { $limit: 2 },
  {
    $project: {
      _id: 0,
      category: "$_id",
      revenue: 1
    }
  }
]).pretty()

Two Minute Drill

  • Aggregation pipeline processes documents through multiple stages.
  • `$match` filters documents (like find).
  • `$group` groups documents and computes aggregates (sum, avg, count).
  • `$project` shapes the output (rename fields, add computed fields).
  • You can combine stages in any order – the output of one stage becomes input to the next.
  • Aggregation is powerful for data analysis, reports, and complex queries.
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