Bucket Aggregation in MongoDB

                                  Bucket Aggregation in MongoDB         

 Introduction

In MongoDB, aggregation operations process data records and return computed results. One of the most powerful and insightful stages of the aggregation pipeline is bucket aggregation. It allows developers to group documents into buckets (ranges), like how we group ages into age groups or prices into cost ranges.

Whether you’re building dashboards, performing data analysis, or summarizing records — bucket aggregation is a go-to feature.

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 Explanation

What is Bucket Aggregation?

Bucket Aggregation in MongoDB groups documents into a number of buckets based on a specific field's values. It's similar to creating histograms where values are divided into intervals or ranges.

There are two main types:

1. $bucket – Manual bucketing using defined boundaries.

2. $bucketAuto – Automatic bucketing based on document distribution.

When to Use Bucket Aggregation?

• Creating histograms

• Analyzing sales by price range

• Grouping users by age

• Categorizing products by rating

• Summarizing data distribution

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 Procedure with Example

🔹 Step 1: Sample Data – Students' Marks

{
  "_id": 1,
  "name": "Alice",
  "marks": 78
}

🔹 Step 2: Using $bucket – Grouping Marks

 Aggregation Query:

db.students.aggregate([
  {
    $bucket: {
      groupBy: "$marks", // field to group by
      boundaries: [0, 50, 70, 85, 100], // custom ranges
      default: "Others", // where unmatched docs go
      output: {
        count: { $sum: 1 },
        names: { $push: "$name" }
      }
    }
  }
])

 Output:

{
  "_id": 70,
  "count": 2,
  "names": ["Alice", "Bob"]
}

 This means two students scored between 70 and 85.

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🔹 Step 3: Using $bucketAuto – Let MongoDB Decide

db.students.aggregate([
  {
    $bucketAuto: {
      groupBy: "$marks",
      buckets: 3,
      output: {
        count: { $sum: 1 },
        avgMarks: { $avg: "$marks" }
      }
    }
  }
])

 Output:

MongoDB evenly distributes students across 3 ranges based on marks.

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 Screenshot 

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 Future Scope

Bucket aggregation is increasingly important as data analysis becomes central to modern applications.

 Future possibilities:

• Integration with AI/ML for smart bucketing

• Enhanced support in MongoDB Charts

• Real-time dashboards using bucket output

• Optimization for large-scale IoT or sensor data

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Conclusion

Bucket Aggregation in MongoDB helps simplify data grouping, analyze trends, and derive useful insights. Whether you're manually setting ranges with $bucket or letting MongoDB handle it with $bucketAuto, this feature boosts the power of your analytics.

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Dhruv karpe

University: Shree Balaji University, Pune

School: School of Computer Studies

Course: BCA (Bachelor of Computer Applications)

Interests: NoSQL, MongoDB, and related technologies

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