Backend with Scalable MongoDB Architecture for a clothing business API

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A production-oriented backend system designed for a clothing e-commerce platform.
This project emphasizes clean architecture, scalable MongoDB data modeling,
RBAC-driven authorization, and a comprehensive automated testing setup using
mongodb-memory-server, vitest, and supertest.

This repository highlights my work on the database system, schema design, validation rules, and backend structure required for real-world e-commerce inventory, user management, and product workflows.

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System Architecture Diagram

Below is a high-level view of how major components interact within the backend:

graph TD
    A[Client App / Postman] -->|HTTP Requests| B(Express API - app.js / routes);
    B -->|Routing & Middleware| C(Controllers);
    C -->|Business Logic| D(Models - Mongoose Schemas);
    D -->|ODM Operations| E[MongoDB];
    
    %% Styling for clarity
    style A fill:#e0e0e0,stroke:#333
    style B fill:#f9f,stroke:#333
    style C fill:#fcc,stroke:#333
    style D fill:#ccf,stroke:#333
    style E fill:#cfc,stroke:#333

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Testing Architecture Overview

This diagram showcases how your test system works using an in-memory MongoDB instance.

graph TD
    A[Vitest - Test Runner Engine] --> B(Supertest - API Request Testing);
    B --> C[Express App];
    C --> D(MongoDB-Memory-Server - In-memory Isolated Database);
    
    style A fill:#ffc,stroke:#333
    style B fill:#ddf,stroke:#333
    style D fill:#fcc,stroke:#333

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Why this matters:

• Zero external dependencies
• Fully isolated environments
• Higher test reliability
• Extremely fast execution

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Features Overview

** Advanced MongoDB Schema Architecture**

The system uses a modular and scalable schema layout supporting:

• Products, Categories, Users, Reviews
• Embedded + referenced document patterns
• Schema-level validation rules
• Schema inheritance for product subtypes
• Timestamps, indexes & lifecycle hooks

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Database Design — Key Highlights

1. Hierarchical Product Modeling

Products share a base schema, while specialized product types extend it:

• Top-wear
• Bottom-wear

Each subtype inherits common product fields and adds category-specific attributes. Examples include:

• Top-wear: sleeve, neckline
• Bottom-wear: length, rise

This structure supports rich filtering and scalable product categorization.

2. Rich Variant & Sizing Structure

Each product supports:

• Multiple variants (e.g., colors)
• Dynamic size arrays
• Stock tracking per size
• Style objects per product type

3. User Model With Production-Grade Features

The User schema includes:

• Secure password hashing
• Token rotation system
• Multiple addresses per user
• Data sanitization
• Schema-level validation rules
• Role-based access control (RBAC)

Admin and customer roles enforce access boundaries throughout the application.

4. Category-Level Organization

Categories are stored as separate documents, enabling structured filtering and
category-specific product queries.

5. Data Integrity Controls

Across the schemas:

• JSON schema validation for required fields
• Email pattern matching
• Minimum password length enforcement
• Enum restrictions for user roles
• Automatic timestamps
• Optional indexing for performance

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RBAC — Role-Based Access Control

The system uses a clean RBAC structure:

• Admin Users: Can create, update, and manage products & categories
• Customers: Can register, log in, manage addresses, and purchase items

Authorization logic is tied directly to user roles in the database, ensuring strong separation of privileges.

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Automated Testing System

The project includes a dedicated /tests folder covering:

• Authentication & token rotation
• RBAC access control
• Product and category endpoint behavior
• Validation errors & edge cases
• CRUD operations with in-memory MongoDB

Tech Stack for Testing

Tool	Purpose
vitest	Test runner
supertest	HTTP endpoint testing
mongodb-memory-server	In-memory database instance for isolated, fast testing

Why this matters

• No external DB required to run tests
• Tests run in milliseconds
• Ensures consistent, repeatable results
• Suitable for CI/CD integration

The current test suite covers 96 passing tests, ensuring stability and confidence in core functionality.

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Tech Stack

• Node.js, Express
• MongoDB, Mongoose
• Vitest, Supertest
• mongodb-memory-server
• JSON Schema Validation
• JWT-based token and refresh-token strategy

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Folder Structure (Simplified)

./
├── logs/
    └── error.log
├── src/
    ├── config/
    ├── controllers/
    ├── errors/
    ├── middleware/
    ├── models/
    ├── routes/
    ├── utils/
    ├── validators/
    ├── app.js
    └── server.js
├── tests/
    ├── auth.test.js
    ├── product.test.js
    ├── category.test.js
    ├── review.test.js
    └── user.test.js
├── .env
├── .gitignore
└── package.json

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Current Focus

This ReadMe highlights the engineering work that went into building:

• A clean, scalable MongoDB data model
• Security-aware authorization systems
• A reliable, high-coverage automated testing environment