$match and $group

 

By Satyam Singh Rajput
BCA (Cloud & Cybersecurity Specialization), SRI BALAJI UNIVERSITY, Pune

Introduction

MongoDB is a widely used NoSQL database known for its flexibility and ease of use. One of its powerful features is the aggregation framework, which helps in performing data analysis and transformations. As part of my hands-on learning, I explored two essential aggregation operators: $match and $group. This blog explains how I used them through a simple example.

What are $match and $group?

• $match is used to filter documents based on specific conditions. It is similar to the WHERE clause in SQL.
• $group is used to group documents based on a specified field and apply aggregate functions like sum, average, minimum, or maximum.

These two stages are often used together to first narrow down the dataset and then summarize or analyze it.

Creating a Practical Dataset

To make this exercise more relevant, I created a fictional dataset representing internship hours logged by students in different domains (Cloud and Cybersecurity) and companies.

Sample Collection: internships

json

CopyEdit

[

  { "student": "Satyam Singh Rajput", "domain": "Cloud", "hours": 30, "company": "AWS Educate" },

  { "student": "Satyam Singh Rajput", "domain": "Cybersecurity", "hours": 20, "company": "CodeFirst" },

  { "student": "Ravi Verma", "domain": "Cloud", "hours": 25, "company": "AWS Educate" },

  { "student": "Aarav Mehta", "domain": "Cybersecurity", "hours": 15, "company": "CodeFirst" },

  { "student": "Satyam Singh Rajput", "domain": "Cloud", "hours": 10, "company": "CodeFirst" }

]

This structure allowed me to practice filtering and grouping data in a realistic context.

Step-by-Step Execution in mongosh

Step 1: Create and Use Database

Bash

use satyamAggregationDB

Step 2: Insert the Documents

javascript

db.internships.insertMany([...]) // Use the dataset above

Step 3: Apply $match

Objective: Filter documents where the domain is "Cloud".

javascript

db.internships.aggregate([

  { $match: { domain: "Cloud" } }

])

 

 

 

 

 

 

 

 

 

 

 

 

This displays only the internship records related to the Cloud domain.

Step 4: Apply $group

Objective: Calculate the total hours completed by each student.

javascript

db.internships.aggregate([

  { $group: { _id: "$student", totalHours: { $sum: "$hours" } } }

])

This summarizes the total internship hours per student, across all domains.

Step 5: Combine $match and $group

Objective: Calculate internship hours per student for the Cloud domain only.

javascript

db.internships.aggregate([

  { $match: { domain: "Cloud" } },

  { $group: { _id: "$student", cloudHours: { $sum: "$hours" } } }

])

This gives a clear view of each student’s total internship hours in the Cloud domain.

Key Learnings

• $match helps to filter and focus the dataset.
• $group allows summarizing information in a meaningful way.
• Combining both stages provides targeted analysis.
• MongoDB makes it easy to perform analytical tasks with minimal setup and code.

Application in Real Projects

As a student specializing in Cloud and Cybersecurity, I see practical uses of these operations in various areas, such as analyzing server logs, processing IoT data, and generating reports from user activity.

Conclusion

Practicing with MongoDB’s aggregation pipeline has helped me understand how data can be processed and summarized efficiently without switching to another tool. This knowledge is essential for building backend logic in cloud-based applications and real-time analytics systems.

Author:
Satyam Singh Rajput
BCA (Cloud & Cybersecurity Specialization)
SRI BALAJI UNIVERSITY, Pune.