Job Portal Database System — DBMS Mini Project

A modern, high-performance Job Portal web application designed as a college DBMS mini-project. This project emphasizes the advantages of NoSQL over traditional SQL databases, specifically highlighting Embedded Documents, Aggregation Pipelines, and Multikey Indexing using MongoDB.

🚀 Features & Concepts

• Embedded Arrays vs Junction Tables: Job skills are stored as an array within the Job document, eliminating the need for complex, slow JOINs typical in relational databases.
• Advanced Aggregation ($unwind, $group): Real-time analytics pipeline that deconstructs the skills array to calculate market demand across thousands of job listings.
• Multikey Indexing (IXSCAN): Demonstrates massive performance gains (from full collection scans to targeted index scans) when searching for specific job skills.
• Massive Dataset: Pre-seeded with 8,000+ realistic documents sourced from real Indian Job Market Kaggle data (TCS, Wipro, Microsoft, Razorpay, etc.).
• Premium UI: A responsive, dark glassmorphism frontend built with vanilla HTML/JS/CSS for zero configuration overhead.

🛠️ Technology Stack

• Database: MongoDB
• Backend: Node.js, Express.js (v5)
• ODM: Mongoose
• Frontend: HTML5, Vanilla JavaScript, CSS3 (Custom Glassmorphism Design)

📦 Setup & Installation

Prerequisites

1. Ensure Node.js (v18+) is installed.
2. Ensure MongoDB is running locally on port 27017 (can run automatically as a Windows service or via MongoDB Compass).

Instructions

1. Navigate to the project directory: bash cd "dbms mini project"
2. Install dependencies: bash npm install
3. Seed the database with the massive Kaggle dataset (5000+ jobs, auto-generated applicants): bash npm run seed-kaggle (Wait 10-15 seconds for it to parse the CSV and insert 8000+ records).
4. Start the server: bash npm run start
5. Open your browser and go to: http://localhost:3000

📂 Project Structure

├── .env                # Environment variables (Port, MongoDB URI)
├── package.json        # Dependencies & scripts
├── server.js           # Express app, server config, and DB connection
├── seed-kaggle.js      # Advanced data generator and CSV parser
├── seed.js             # Basic initial seed script
├── india_job_market_2024_2026.csv # Raw Kaggle dataset
├── models/             # Mongoose Schemas (Job, Applicant, Application, Company)
├── routes/             # REST API controllers and Aggregation logic
└── public/             # Frontend Single Page Application (HTML, CSS, JS)

📊 Analytics Highlights

The most impressive part of this project is the Aggregation Pipeline found in routes/analytics.js. It runs across 5000+ documents in milliseconds:

// Example: Skill Demand Aggregation
const skillDemand = await Job.aggregate([
  { $unwind: "$skills" }, // Flatten the skills array
  { $group: { _id: "$skills", count: { $sum: 1 } } }, // Count occurrences
  { $sort: { count: -1 } }, // Sort by most demanded
  { $limit: 10 } // Get top 10
]);

Built With

• css
• html
• javascript
• mongodb
• react