$mergeObjects

The $mergeObjects operator combines multiple documents into a single document. The mergeObjects operation is used in aggregation pipelines to merge fields from different documents or add one or more fields to an existing document. The operator overwrites fields in the target document with fields from the source document when conflicts occur.

Syntax

{
  $mergeObjects: [ < document1 > , < document2 > , ...]
}

Parameters

Examples

Let's understand the usage with sample json from stores dataset.

{
    "_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 - Merging documents as an accumulator to group documents by the sales subdocument

The query is an aggregation pipeline that uses $mergeObjects to merge all sales subdocuments per city for a specific company.

db.stores.aggregate([
  {
    $match: {
      company: "Fourth Coffee"
    }
  },
  {
    $group: {
      _id: "$city",
      mergedSales: {
        $mergeObjects: "$sales"
      }
    }
  },
  {
    $limit: 2   // returns only the first 3 grouped cities
  }
])

The query groups store documents by city for the company "Fourth Coffee" and merges their sales fields into a single object per city.

[
  {
      "_id": "Jalonborough",
      "mergedSales": {
          "totalSales": 45747,
          "salesByCategory": [
              {
                  "categoryName": "Bucket Bags",
                  "totalSales": 45747
              }
          ]
      }
  },
  {
      "_id": "Port Vladimir",
      "mergedSales": {
          "totalSales": 32000,
          "salesByCategory": [
              {
                  "categoryName": "DJ Speakers",
                  "totalSales": 24989
              },
              {
                  "categoryName": "DJ Cables",
                  "totalSales": 7011
              }
          ]
      }
  }
]

Related content

• Review options for migrating from MongoDB to Azure Cosmos DB for MongoDB (vCore).
• Read more about feature compatibility with MongoDB.