MongoDB $match Stage

Example 1 — Sample Data

Five products with name, category, status, price, and cost. We load them into a collection named products.

db.products.drop()

db.products.insertMany([
  { _id: 1, name: "Notebook", category: "Books",   status: "active",  price: 12, cost: 5 },
  { _id: 2, name: "Keyboard", category: "Gadgets", status: "active",  price: 45, cost: 20 },
  { _id: 3, name: "Mug",      category: "Home",    status: "active",  price: 8,  cost: 2 },
  { _id: 4, name: "Old HD",   category: "Gadgets", status: "retired", price: 40, cost: 10 },
  { _id: 5, name: "Cable",    category: "Gadgets", status: "active",  price: 6,  cost: 8 }
])

How the Data Is Stored in MongoDB

After the insert, MongoDB keeps five documents in products. Run find() to see them:

db.products.find()

[
  { _id: 1, name: "Notebook", category: "Books",   status: "active",  price: 12, cost: 5 },
  { _id: 2, name: "Keyboard", category: "Gadgets", status: "active",  price: 45, cost: 20 },
  { _id: 3, name: "Mug",      category: "Home",    status: "active",  price: 8,  cost: 2 },
  { _id: 4, name: "Old HD",   category: "Gadgets", status: "retired", price: 40, cost: 10 },
  { _id: 5, name: "Cable",    category: "Gadgets", status: "active",  price: 6,  cost: 8 }
]

Each product document uses these fields:

products (5 rows):

  name       category   status    price  cost
  --------   --------   -------   -----  ----
  Notebook   Books      active      12     5
  Keyboard   Gadgets    active      45    20
  Mug        Home       active       8     2
  Old HD     Gadgets    retired     40    10
  Cable      Gadgets    active       6     8

Next examples filter this same set with $match.

How It Works

1. Reset once. drop() clears old rows; insertMany loads the five products.
2. Stable ids. _id 1–5 makes it easy to spot documents in output.
3. find() confirms storage. You see the same fields the pipeline will read.
4. Reuse. Examples 2–4 assume this data is already in products.

Example 2 — Equality + Comparison

Goal: keep active products with price at least 20 (uses the products data from Example 1).

db.products.aggregate([
  {
    $match: {
      status: "active",
      price: { $gte: 20 }
    }
  }
])

{ _id: 2, name: "Keyboard", category: "Gadgets", status: "active", price: 45, cost: 20 }
// Notebook/Mug: price too low
// Old HD: retired
// Cable: price < 20

How It Works

1. Same five products. Pipeline reads the documents you inserted in Example 1.
2. Equality. status: "active" drops retired items (Old HD).
3. Comparison. price: { $gte: 20 } keeps mid/high prices.
4. AND by default. Fields in one object must all match—only Keyboard passes.

Example 3 — $in and $or

Goal: keep Books or Home, or any product whose status is in a short list (still on the Example 1 products collection).

db.products.aggregate([
  {
    $match: {
      $or: [
        { category: { $in: ["Books", "Home"] } },
        { status: { $in: ["active", "featured"] } }
      ]
    }
  },
  { $project: { name: 1, category: 1, status: 1, _id: 0 } },
  { $sort: { name: 1 } }
])

Books + Home always match (first $or branch)
Active Gadgets also match (second branch)
$in = "value is one of these"
$or  = "any branch may pass"

How It Works

1. Example 1 data. All five products enter the pipeline.
2. $in. First branch: category Books or Home (Notebook, Mug).
3. $or. Second branch: status active or featured (adds active Gadgets).
4. Project + sort. Slim fields and alphabetical names for readable output.

Example 4 — $expr Cross-Field

Goal: keep products that sell above cost (margin check across two fields on the Example 1 documents).

db.products.aggregate([
  {
    $match: {
      $expr: { $gt: ["$price", "$cost"] }
    }
  },
  {
    $project: {
      name: 1,
      price: 1,
      cost: 1,
      margin: { $subtract: ["$price", "$cost"] },
      _id: 0
    }
  }
])

{ name: "Notebook", price: 12, cost: 5, margin: 7 }
{ name: "Keyboard", price: 45, cost: 20, margin: 25 }
{ name: "Mug", price: 8, cost: 2, margin: 6 }
{ name: "Old HD", price: 40, cost: 10, margin: 30 }
// Cable dropped: price 6 < cost 8

How It Works

1. Example 1 rows. Each product brings its own price and cost.
2. $expr. Lets aggregation operators run inside $match.
3. Field paths. "$price" and "$cost" are compared per document.
4. Cable dropped. Price 6 < cost 8; others pass and show computed margin.