Project field in MongoDB

To project field in MongoDB, use $project in an aggregation pipeline. The $project stage allows you to reshape documents by including, excluding, or creating new fields.

Syntax

db.collection.aggregate([
    {
        $project: {
            "fieldName": 1,           // Include field
            "newField": "$existingField",  // Rename field
            "_id": 0                  // Exclude _id
        }
    }
]);

Sample Data

db.demo439.insertMany([
    {
        "Name": "Chris",
        "MarksInformation": {
            "Marks1": 67,
            "Marks2": 45,
            "Marks3": 78
        }
    },
    {
        "Name": "David",
        "MarksInformation": {
            "Marks1": 50,
            "Marks2": 57,
            "Marks3": 68
        }
    },
    {
        "Name": "Bob",
        "MarksInformation": {
            "Score1": 65,
            "Score2": 71
        }
    }
]);

{
    "acknowledged": true,
    "insertedIds": [
        ObjectId("5e77833abbc41e36cc3caeab"),
        ObjectId("5e77833abbc41e36cc3caeac"),
        ObjectId("5e77833bbbc41e36cc3caead")
    ]
}

Display All Documents

db.demo439.find();

{ "_id" : ObjectId("5e77833abbc41e36cc3caeab"), "Name" : "Chris", "MarksInformation" : { "Marks1" : 67, "Marks2" : 45, "Marks3" : 78 } }
{ "_id" : ObjectId("5e77833abbc41e36cc3caeac"), "Name" : "David", "MarksInformation" : { "Marks1" : 50, "Marks2" : 57, "Marks3" : 68 } }
{ "_id" : ObjectId("5e77833bbbc41e36cc3caead"), "Name" : "Bob", "MarksInformation" : { "Score1" : 65, "Score2" : 71 } }

Example: Project Fields with Conditional Logic

This query normalizes different field names (Marks vs Score) into a consistent output ?

db.demo439.aggregate([
    {
        $project: {
            "Name": 1,
            "Marks1": {
                $cond: [
                    { $eq: [{ $ifNull: ["$MarksInformation.Marks1", 0] }, 0] },
                    { $ifNull: ["$MarksInformation.Score1", 0] },
                    "$MarksInformation.Marks1"
                ]
            },
            "Marks2": {
                $cond: [
                    { $eq: [{ $ifNull: ["$MarksInformation.Marks2", 0] }, 0] },
                    { $ifNull: ["$MarksInformation.Score2", 0] },
                    "$MarksInformation.Marks2"
                ]
            },
            "Marks3": {
                $cond: [
                    { $eq: [{ $ifNull: ["$MarksInformation.Marks3", 0] }, 0] },
                    { $ifNull: ["$MarksInformation.Score3", 0] },
                    "$MarksInformation.Marks3"
                ]
            }
        }
    }
]);

{ "_id" : ObjectId("5e77833abbc41e36cc3caeab"), "Name" : "Chris", "Marks1" : 67, "Marks2" : 45, "Marks3" : 78 }
{ "_id" : ObjectId("5e77833abbc41e36cc3caeac"), "Name" : "David", "Marks1" : 50, "Marks2" : 57, "Marks3" : 68 }
{ "_id" : ObjectId("5e77833bbbc41e36cc3caead"), "Name" : "Bob", "Marks1" : 65, "Marks2" : 71, "Marks3" : 0 }

How It Works

• $cond performs conditional logic (if-else)
• $ifNull returns a default value when a field is null or missing
• $eq checks if values are equal
• The query checks if "Marks" fields exist; if not, it uses "Score" fields instead

Conclusion

The $project stage in MongoDB's aggregation pipeline allows you to reshape documents by selecting, renaming, and creating fields. Combined with conditional operators like $cond, you can normalize data with different field structures.