Using aggregation pipeline to fetch records in MongoDB

The MongoDB aggregation pipeline processes documents through multiple stages, where each stage transforms the documents as they pass through the pipeline. It's a powerful framework for data analysis and transformation operations.

Syntax

db.collection.aggregate([
    { $stage1: { /* stage operations */ } },
    { $stage2: { /* stage operations */ } },
    { $stageN: { /* stage operations */ } }
])

Sample Data

db.demo218.insertMany([
    {"Name": "Chris", "Branch": "CS", "Marks": [65, 78, 36, 90]},
    {"Name": "David", "Branch": "ME", "Marks": [56, 45, 42, 51]},
    {"Name": "Chris", "Branch": "CS", "Marks": [78, 65, 89]}
]);

{
    "acknowledged": true,
    "insertedIds": [
        ObjectId("5e3e5f4903d395bdc2134712"),
        ObjectId("5e3e5f6203d395bdc2134713"),
        ObjectId("5e3e5f6c03d395bdc2134714")
    ]
}

Display all documents from the collection ?

db.demo218.find();

{ "_id": ObjectId("5e3e5f4903d395bdc2134712"), "Name": "Chris", "Branch": "CS", "Marks": [65, 78, 36, 90] }
{ "_id": ObjectId("5e3e5f6203d395bdc2134713"), "Name": "David", "Branch": "ME", "Marks": [56, 45, 42, 51] }
{ "_id": ObjectId("5e3e5f6c03d395bdc2134714"), "Name": "Chris", "Branch": "CS", "Marks": [78, 65, 89] }

Example: Find CS Students with Marks > 88

Use aggregation pipeline to fetch CS branch students having marks greater than 88 ?

db.demo218.aggregate([
    { "$unwind": "$Marks" },
    { "$match": {
        "Branch": "CS",
        "Marks": { "$gt": 88 }
    }},
    { "$group": {
        "_id": "$_id",
        "Name": { "$first": "$Name" },
        "Branch": { "$first": "$Branch" },
        "Marks": { "$first": "$Marks" }
    }}
]);

{ "_id": ObjectId("5e3e5f6c03d395bdc2134714"), "Name": "Chris", "Branch": "CS", "Marks": 89 }
{ "_id": ObjectId("5e3e5f4903d395bdc2134712"), "Name": "Chris", "Branch": "CS", "Marks": 90 }

How It Works

• $unwind: Deconstructs the Marks array, creating separate documents for each mark
• $match: Filters documents where Branch is "CS" and Marks > 88
• $group: Groups results back by document _id, keeping the first matching mark

Conclusion

MongoDB aggregation pipeline enables complex data processing through sequential stages. Use $unwind, $match, and $group stages to transform array data and apply filtering conditions effectively.