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Arjun Kumar
Arjun Kumar

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Mastering MongoDB Aggregation: 10 Practical Examples with Sample Data

#mongodb #aggregationpipeline #fullstackdevelopmen #nosql

Image descriptionMongoDB's aggregation framework is powerful yet often misunderstood. Whether you're prepping for interviews, building analytics, or learning data science workflows, mastering aggregation is essential. Below are 10 real-world problems, complete with queries and explanations, using a sample dataset of users.

Sample MongoDB Collection: users 

[ { "name": "Alice", "age": 28, "country": "USA", "gender": "female", "signupDate": "2023-12-20T10:15:00Z" }, { "name": "Bob", "age": 34, "country": "Canada", "gender": "male", "signupDate": "2024-01-15T12:00:00Z" }, { "name": "Charlie", "age": 22, "country": "India", "gender": "male", "signupDate": "2024-02-05T09:45:00Z" }, { "name": "Diana", "age": 30, "country": "USA", "gender": "female", "signupDate": "2024-03-01T08:30:00Z" }, { "name": "Eve", "age": 29, "country": "India", "gender": "female", "signupDate": "2024-01-25T11:20:00Z" }, { "name": "Frank", "age": 40, "country": "Canada", "gender": "male", "signupDate": "2024-02-10T14:50:00Z" }, { "name": "Grace", "age": 19, "country": "USA", "gender": "female", "signupDate": "2024-03-10T07:10:00Z" }, { "name": "Harry", "age": 31, "country": "UK", "gender": "male", "signupDate": "2024-03-12T17:40:00Z" }, { "name": "Ivy", "age": 27, "country": "India", "gender": "female", "signupDate": "2024-01-01T06:00:00Z" }, { "name": "Jack", "age": 36, "country": "Canada", "gender": "male", "signupDate": "2024-02-20T18:15:00Z" } ]
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1. Count of Users by Country 

[ { $group: { _id: "$country", totalUsers: { $sum: 1 } } } ]
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2. Average Age by Gender 

[ { $group: { _id: "$gender", averageAge: { $avg: "$age" } } } ]
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3. Users Signed Up After February 1, 2024 

[ { $addFields: { signupDateConverted: { $toDate: "$signupDate" } } }, { $match: { signupDateConverted: { $gte: ISODate("2024-02-01T00:00:00Z") } } } ]
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4. Count of Female Users by Country 

[ { $match: { gender: "female" } }, { $group: { _id: "$country", femalePerCountry: { $sum: 1 } } } ]
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5. Group by Country After Sorting by Signup Date (Descending) 

[ { $sort: { signupDate: -1 } }, { $group: { _id: "$country" } } ]
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6. Get the 3 Earliest Signups 

[ { $sort: { signupDate: 1 } }, { $limit: 3 } ]
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7. All Indian Users Sorted by Age Descending 

[ { $match: { country: "India" } }, { $sort: { age: -1 } } ]
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8. Count of Users Grouped by Month of Signup 

[ { $project: { monthYear: { $dateToString: { format: "%Y-%m", date: { $toDate: "$signupDate" } } } } }, { $group: { _id: "$monthYear", userCount: { $sum: 1 } } }, { $sort: { _id: 1 } } ]
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9. Average Age of Users Signed Up in 2024 

[ { $project: { year: { $dateToString: { format: "%Y", date: { $toDate: "$signupDate" } } }, age: 1, name: 1, signupDate: 1 } }, { $match: { year: "2024" } }, { $group: { _id: "$year", avgAge: { $avg: "$age" } } } ]
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10. Count of Users Aged 25 to 35 Grouped by Gender 

[ { $match: { age: { $gte: 25, $lte: 35 } } }, { $group: { _id: "$gender", count: { $sum: 1 } } } ]
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MongoDB aggregations are incredibly versatile. From basic groupings to complex date manipulations, they can power dashboards, reports, and analytics. Bookmark this post for reference and practice daily!

Happy querying! 🚀

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