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Feature: MongoDB Database Design and Integration

Pritika K requested to merge backend-development into main

Merge Request: Database Layer Implementation – RoadPulse AI

Summary

This merge request introduces the complete database layer for the RoadPulse AI – Smart Road Maintenance & Monitoring System. The implementation includes MongoDB Atlas integration, Mongoose models, schema validation, indexing, and secure database configuration to support complaint management, user administration, and maintenance operations.

Changes Implemented

Database Configuration

  • Configured MongoDB Atlas connection.
  • Created centralized database connection module.
  • Added environment-based database configuration.
  • Implemented connection error handling and logging.

Data Models

  • Created User schema and model.
  • Created Complaint schema and model.
  • Created MaintenanceWorker schema and model.
  • Defined relationships between collections using ObjectId references.

Validation & Constraints

  • Added required field validation.
  • Implemented enum validations for status and roles.
  • Added unique constraints where applicable.
  • Configured default values and field sanitization.

Performance Optimization

  • Added timestamps for auditing and tracking.
  • Implemented database indexes for frequently queried fields.
  • Optimized schema structure for efficient CRUD operations.

Security

  • Secured database credentials using environment variables.
  • Added .env.example for setup guidance.
  • Prevented hardcoded sensitive information.

Features Added

  • Centralized database connection management.
  • User management with role-based access support.
  • Complaint registration and tracking system.
  • Maintenance worker assignment support.
  • Complaint priority and status monitoring.
  • Relationship mapping between users and complaints.
  • Scalable schema design for future enhancements.
  • Improved query performance through indexing.

Files Added / Updated

Added

  • config/database.js
  • models/User.js
  • models/Complaint.js
  • models/MaintenanceWorker.js
  • .env.example
  • Database documentation files

Updated

  • Backend configuration files (if applicable)

Testing Performed

Database Connection

  • Verified successful MongoDB Atlas connection.
  • Tested connection failure handling.

Schema Validation

  • Verified required field validation.
  • Tested enum and constraint validations.
  • Confirmed duplicate record prevention.

CRUD Operations

  • Created, read, updated, and deleted records for:

    • Users
    • Complaints
    • Maintenance Workers

Relationship Validation

  • Verified user-to-complaint references.
  • Tested complaint assignment workflows.

Performance Testing

  • Confirmed index creation.
  • Validated query execution performance.

Impact

This implementation establishes the foundational data layer for the RoadPulse AI platform. It enables reliable data storage, complaint lifecycle management, user administration, and maintenance operations while supporting future scalability, analytics, reporting, and AI-driven insights.

Deployment Notes

  1. Configure MongoDB Atlas connection string in .env.
  2. Install project dependencies.
  3. Verify database connectivity before deployment.
  4. Ensure indexes are created during application startup.
  5. Review environment variables for production deployment.

Checklist

  • MongoDB Atlas connected successfully
  • Database configuration module created
  • User schema implemented
  • Complaint schema implemented
  • Maintenance Worker schema implemented
  • Validation rules added
  • Indexes configured
  • Environment variables secured
  • CRUD operations tested
  • Documentation updated closses #3
Edited by Pritika K

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