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The Resume Generator uses MongoDB as its database to store user accounts, interview reports, and authentication tokens. This guide covers MongoDB setup and the application’s data models.

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MongoDB Installation

1

Choose your MongoDB deployment

You can use either:
  • MongoDB Atlas (cloud-hosted, recommended for production)
  • Local MongoDB (self-hosted, good for development)
2

Install MongoDB (Local Option)

For local development, install MongoDB Community Edition:macOS:
brew tap mongodb/brew
brew install mongodb-community
brew services start mongodb-community
Linux (Ubuntu/Debian):
sudo apt-get install mongodb-org
sudo systemctl start mongod
sudo systemctl enable mongod
Windows: Download and install from MongoDB Download Center
3

Create Database (Local)

MongoDB will automatically create the database on first connection. The default name is based on your MONGO_URI:
# Database will be created automatically
# Default: resume-generator
4

Set up MongoDB Atlas (Cloud Option)

  1. Create a free account at MongoDB Atlas
  2. Create a new cluster
  3. Add a database user with read/write permissions
  4. Whitelist your IP address (or use 0.0.0.0/0 for development)
  5. Get your connection string from the “Connect” button
5

Configure connection string

Add your MongoDB URI to .env:
# Local MongoDB
MONGO_URI=mongodb://localhost:27017/resume-generator

# MongoDB Atlas
MONGO_URI=mongodb+srv://<username>:<password>@cluster.mongodb.net/resume-generator

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Database Connection

The application establishes a database connection using Mongoose in src/config/database.js: 
const mongoose = require("mongoose")

async function connectToDB() {
    try {
        await mongoose.connect(process.env.MONGO_URI)
        console.log("Connected to Database")
    }
    catch (err) {
        console.log(err)
    }
}

module.exports = connectToDB
The connection is initialized in server.js before starting the Express server.

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Database Schema

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User Model

Stores user account information for authentication. Location: src/models/user.model.js Schema Structure:
​
username
string
required
Unique username for the user account
  • Must be unique across all users
  • Error message: “username already taken”
​
email
string
required
User’s email address
  • Must be unique across all users
  • Used for login authentication
  • Error message: “Account already exists with this email address”
​
password
string
required
Hashed password (bcrypt with 10 salt rounds)
  • Never stored in plain text
  • Hashed before saving to database
Collection Name: users Example Document: 
{
  "_id": "507f1f77bcf86cd799439011",
  "username": "johndoe",
  "email": "[email protected]",
  "password": "$2a$10$N9qo8uLOickgx2ZMRZoMye..."
}

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Interview Report Model

Stores AI-generated interview preparation reports for users. Location: src/models/interviewReport.model.js Schema Structure:
​
title
string
required
Job title for which the interview report was generated
​
jobDescription
string
required
The job description provided by the user
​
resume
string
Resume text content (optional if resume file was uploaded)
​
selfDescription
string
User’s self-description or cover letter
​
matchScore
number
AI-calculated score (0-100) indicating job match quality
​
technicalQuestions
array
Array of technical interview questionsEach question contains:
  • question (string, required): The interview question
  • intention (string, required): Why the interviewer asks this
  • answer (string, required): How to answer effectively
​
behavioralQuestions
array
Array of behavioral interview questionsEach question contains:
  • question (string, required): The interview question
  • intention (string, required): Why the interviewer asks this
  • answer (string, required): How to answer effectively
​
skillGaps
array
Identified gaps between user skills and job requirementsEach skill gap contains:
  • skill (string, required): The missing or weak skill
  • severity (enum, required): “low”, “medium”, or “high”
​
preparationPlan
array
Day-by-day interview preparation planEach day contains:
  • day (number, required): Day number (starting from 1)
  • focus (string, required): Main focus area for the day
  • tasks (array of strings, required): Specific tasks to complete
​
user
ObjectId
required
Reference to the user who created this report
  • References the users collection
​
timestamps
object
Automatically managed timestamps
  • createdAt: When the report was generated
  • updatedAt: When the report was last modified
Collection Name: interviewreports Example Document: 
{
  "_id": "507f1f77bcf86cd799439012",
  "title": "Senior Full Stack Developer",
  "jobDescription": "We are seeking a Senior Full Stack Developer...",
  "resume": "Experienced software engineer with 5+ years...",
  "selfDescription": "Passionate developer with expertise in...",
  "matchScore": 85,
  "technicalQuestions": [
    {
      "question": "Explain the difference between REST and GraphQL",
      "intention": "Assess understanding of API design patterns",
      "answer": "REST is resource-based while GraphQL is query-based..."
    }
  ],
  "behavioralQuestions": [...],
  "skillGaps": [
    {
      "skill": "Kubernetes",
      "severity": "medium"
    }
  ],
  "preparationPlan": [
    {
      "day": 1,
      "focus": "System Design Fundamentals",
      "tasks": [
        "Review microservices architecture patterns",
        "Practice designing a URL shortener"
      ]
    }
  ],
  "user": "507f1f77bcf86cd799439011",
  "createdAt": "2024-03-15T10:30:00.000Z",
  "updatedAt": "2024-03-15T10:30:00.000Z"
}

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Token Blacklist Model

Stores invalidated JWT tokens (e.g., after logout) to prevent reuse. Location: src/models/blacklist.model.js Schema Structure:
​
token
string
required
The JWT token that has been invalidated
  • Added when users log out
  • Checked during authentication to reject blacklisted tokens
​
timestamps
object
Automatically managed timestamps
  • createdAt: When the token was blacklisted
  • updatedAt: When the record was last modified
Collection Name: blacklisttokens Example Document: 
{
  "_id": "507f1f77bcf86cd799439013",
  "token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...",
  "createdAt": "2024-03-15T14:20:00.000Z",
  "updatedAt": "2024-03-15T14:20:00.000Z"
}

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Database Relationships

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Database Indexing

The application automatically creates indexes on:
  • users.username (unique)
  • users.email (unique)
  • interviewreports.user (reference)

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Troubleshooting

  • Verify MongoDB is running: sudo systemctl status mongod
  • Check firewall settings allow port 27017
  • For Atlas, verify IP whitelist includes your current IP
  • Test connection using MongoDB Compass
  • Username or email already exists in database
  • Clear test data if in development: db.users.deleteMany({})
  • Check for duplicate entries before insertion
  • Ensure MongoDB user has readWrite role
  • Verify credentials in connection string
  • Check database name matches your configuration
  • Review required fields in error messages
  • Ensure data types match schema definitions
  • Check enum values for skillGaps.severity

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Database Management

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Using MongoDB Shell

Connect to your database: 
mongosh "mongodb://localhost:27017/resume-generator"
Common commands: 
// List all collections
show collections

// View users
db.users.find()

// View interview reports
db.interviewreports.find()

// Count documents
db.users.countDocuments()

// Delete all data (development only)
db.users.deleteMany({})
db.interviewreports.deleteMany({})
db.blacklisttokens.deleteMany({})

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Using MongoDB Compass

MongoDB Compass provides a GUI for database management:
  1. Download from MongoDB Compass
  2. Connect using your MONGO_URI
  3. Browse collections and documents visually
  4. Create indexes and run queries

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Next Steps

Environment Variables

Configure all required environment variables

AI Setup

Set up Google Gemini API for AI features

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