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$group

The $group stage groups documents by specified identifier expressions and applies accumulator expressions.

$group

The $group aggregation stage groups documents by specified identifier expressions and applies accumulator expressions to create computed fields for each group. This stage is essential for data aggregation and summarization operations.

Syntax

{
  $group: {
    _id: <expression>,
    <field1>: { <accumulator1>: <expression1> },
    <field2>: { <accumulator2>: <expression2> }
  }
}

Parameters

ParameterDescription
_idRequired. The expression to group by. Use null to calculate accumulated values for all input documents.
fieldOptional. Computed using accumulator operators like $sum, $avg, $max, $min, $count, etc.

Examples

Consider this sample document from the stores collection.

{
  "_id": "2cf3f885-9962-4b67-a172-aa9039e9ae2f",
  "name": "First Up Consultants | Bed and Bath Center - South Amir",
  "location": {
    "lat": 60.7954,
    "lon": -142.0012
  },
  "staff": {
    "totalStaff": {
      "fullTime": 18,
      "partTime": 17
    }
  },
  "sales": {
    "totalSales": 37701,
    "salesByCategory": [
      {
        "categoryName": "Mattress Toppers",
        "totalSales": 37701
      }
    ]
  },
  "promotionEvents": [
    {
      "eventName": "Price Drop Palooza",
      "promotionalDates": {
        "startDate": {
          "Year": 2024,
          "Month": 9,
          "Day": 21
        },
        "endDate": {
          "Year": 2024,
          "Month": 9,
          "Day": 30
        }
      },
      "discounts": [
        {
          "categoryName": "Bath Accessories",
          "discountPercentage": 18
        },
        {
          "categoryName": "Pillow Top Mattresses",
          "discountPercentage": 17
        }
      ]
    }
  ]
}

Example 1: Group staffing analysis by city

This query groups stores by city and analyzes the staffing patterns across different locations.

db.stores.aggregate([
  {
    $group: {
      _id: "$city",
      totalFullTimeStaff: { $sum: "$staff.employeeCount.fullTime" },
      totalPartTimeStaff: { $sum: "$staff.employeeCount.partTime" },
      avgFullTimeStaff: { $avg: "$staff.employeeCount.fullTime" },
      storesInCity: { $count: {} }
    }
  },
  {
    $project: {
      city: "$_id",
      totalFullTimeStaff: 1,
      totalPartTimeStaff: 1,
      avgFullTimeStaff: { $round: ["$avgFullTimeStaff", 1] },
      storesInCity: 1,
      fullTimeRatio: {
        $round: [
          { $divide: ["$totalFullTimeStaff", { $add: ["$totalFullTimeStaff", "$totalPartTimeStaff"] }] },
          2
        ]
      }
    }
  },
  { $limit : 2}
])

The first two results returned by this query are:

[
  {
    "_id": "New Ellsworth",
    "totalFullTimeStaff": 11,
    "totalPartTimeStaff": 1,
    "avgFullTimeStaff": 11,
    "storesInCity": 1,
    "city": "New Ellsworth",
    "fullTimeRatio": 0.92
  },
  {
    "_id": "Jalonborough",
    "totalFullTimeStaff": 4,
    "totalPartTimeStaff": 1,
    "avgFullTimeStaff": 4,
    "storesInCity": 1,
    "city": "Jalonborough",
    "fullTimeRatio": 0.8
  }
]

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