MongoDB aggregate() and Aggregation Pipeline

Relax. We'll talk through this in plain words — no textbook voice.

What you'll walk away with

Build an aggregation pipeline
Understand stage ordering
Use group accumulators

Let's break it down simply

In an aggregation pipeline, documents pass through each stage in order. Putting $match early trims the data down before $group does its counting and grouping. aggregate() doesn't touch the underlying collection data unless $out or $merge is used.

javascript

db.orders.aggregate([
  { $match: { status: 'paid' } },
  {
    $group: {
      _id: '$customerId',
      orderCount: { $sum: 1 },
      totalSpent: { $sum: '$total' }
    }
  },
  { $match: { totalSpent: { $gte: 100000 } } },
  { $sort: { totalSpent: -1 } },
  { $project: { _id: 0, customerId: '$_id', orderCount: 1, totalSpent: 1 } }
])

You should see

{ customerId: 42, orderCount: 3, totalSpent: 185000 }

Try it yourself

Group the products collection by category and calculate the average price and product count.

Aggregation Pipeline — MongoDB

Easy traps

Doing all your filtering only after $group, forcing far more documents than necessary to pass through the pipeline
Forgetting to put a $ in front of a field path

Exercise

Group the products collection by category and calculate the average price and product count.

You'll know it worked when: { customerId: 42, orderCount: 3, totalSpent: 185000 }