$facet

The $facet stage aggregation pipelines allow for multiple parallel aggregations to be executed within a single pipeline stage. It's useful for performing multiple analyses on the same dataset in a single query.

Syntax

The syntax for the $facet stage is as follows:

{
  "$facet": {
    "outputField1": [ { "stage1": {} }, { "stage2": {} } ],
    "outputField2": [ { "stage1": {} }, { "stage2": {} } ]
  }
}

Parameters

Parameter Description
outputFieldN The name of the output field.
stageN The aggregation stage to be executed.

Examples

Consider this sample document from the stores collection.

{
    "_id": "0fcc0bf0-ed18-4ab8-b558-9848e18058f4",
    "name": "First Up Consultants | Beverage Shop - Satterfieldmouth",
    "location": {
        "lat": -89.2384,
        "lon": -46.4012
    },
    "staff": {
        "totalStaff": {
            "fullTime": 8,
            "partTime": 20
        }
    },
    "sales": {
        "totalSales": 75670,
        "salesByCategory": [
            {
                "categoryName": "Wine Accessories",
                "totalSales": 34440
            },
            {
                "categoryName": "Bitters",
                "totalSales": 39496
            },
            {
                "categoryName": "Rum",
                "totalSales": 1734
            }
        ]
    },
    "promotionEvents": [
        {
            "eventName": "Unbeatable Bargain Bash",
            "promotionalDates": {
                "startDate": {
                    "Year": 2024,
                    "Month": 6,
                    "Day": 23
                },
                "endDate": {
                    "Year": 2024,
                    "Month": 7,
                    "Day": 2
                }
            },
            "discounts": [
                {
                    "categoryName": "Whiskey",
                    "discountPercentage": 7
                },
                {
                    "categoryName": "Bitters",
                    "discountPercentage": 15
                },
                {
                    "categoryName": "Brandy",
                    "discountPercentage": 8
                },
                {
                    "categoryName": "Sports Drinks",
                    "discountPercentage": 22
                },
                {
                    "categoryName": "Vodka",
                    "discountPercentage": 19
                }
            ]
        },
        {
            "eventName": "Steal of a Deal Days",
            "promotionalDates": {
                "startDate": {
                    "Year": 2024,
                    "Month": 9,
                    "Day": 21
                },
                "endDate": {
                    "Year": 2024,
                    "Month": 9,
                    "Day": 29
                }
            },
            "discounts": [
                {
                    "categoryName": "Organic Wine",
                    "discountPercentage": 19
                },
                {
                    "categoryName": "White Wine",
                    "discountPercentage": 20
                },
                {
                    "categoryName": "Sparkling Wine",
                    "discountPercentage": 19
                },
                {
                    "categoryName": "Whiskey",
                    "discountPercentage": 17
                },
                {
                    "categoryName": "Vodka",
                    "discountPercentage": 23
                }
            ]
        }
    ]
}

Example 1: Faceted search on sales and promotions

To perform simultaneous analyses on sales and promotions, for specified product categories. The salesAnalysis pipeline unwinds the salesByCategory, filters for certain categories, and groups them to sum totalSales. The promotion analysis pipeline unwinds promotional events and their discounts, filters for specific categories like Laptops, Smartphones etc., and groups them to calculate the average discount percentage. The input documents from stores collection are fetched from the database only once, at the beginning of this operation.

db.stores.aggregate([
  {
    $facet: {
      salesAnalysis: [
        { $unwind: "$sales.salesByCategory" },
        { $match: { "sales.salesByCategory.categoryName": { $in: ["Laptops", "Smartphones", "Cameras", "Watches"] } } },
        { $group: { _id: "$sales.salesByCategory.categoryName", totalSales: { $sum: "$sales.salesByCategory.totalSales" } } }
      ],
      promotionAnalysis: [
        { $unwind: "$promotionEvents" },
        { $unwind: "$promotionEvents.discounts" },
        { $match: { "promotionEvents.discounts.categoryName": { $in: ["Laptops", "Smartphones", "Cameras", "Watches"] } } },
        { $group: { _id: "$promotionEvents.discounts.categoryName", avgDiscount: { $avg: "$promotionEvents.discounts.discountPercentage" } } }
      ]
    }
  }
]).pretty();

The returned output from query displays the aggregated insights.

{
  "salesAnalysis": [
    { "_id": "Smartphones", "totalSales": 440815 },
    { "_id": "Laptops", "totalSales": 679453 },
    { "_id": "Cameras", "totalSales": 481171 },
    { "_id": "Watches", "totalSales": 492299 }
  ],
  "promotionAnalysis": [
    { "_id": "Smartphones", "avgDiscount": 14.32 },
    { "_id": "Laptops", "avgDiscount": 14.780645161290323 },
    { "_id": "Cameras", "avgDiscount": 15.512195121951219 },
    { "_id": "Watches", "avgDiscount": 15.174418604651162 }
  ]
}

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