QEats

🍽️ QEats - Food Delivery Backend System

QEats is a scalable backend system built with Spring Boot and MongoDB to serve restaurant data efficiently based on user geolocation and request context. Designed for high performance and modularity, the system integrates REST APIs, layered architecture, and test-driven development practices to support dynamic food discovery in real time.

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📌 1. Problem Statement

The aim is to build the backend for an online food discovery platform (like Zomato/Swiggy) that:

• Serves restaurant and menu item data based on a user’s geolocation.
• Returns results based on request time and restaurant availability.
• Ensures performance and accuracy in a high-traffic environment.
• Utilizes modular architecture for easy testing, maintenance, and scaling.

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🧰 2. Technology Stack & Rationale

Technology	Purpose
Java 11	Backend programming language
Spring Boot	Framework for building RESTful web services
MongoDB	NoSQL database for storing flexible restaurant/item data
Gradle	Build automation and dependency management
JUnit & Mockito	Unit testing and mock testing
Log4j2	Logging and error tracking
Shell Scripts (setup_mongo.sh)	Setup mock data for local dev/testing

Why MongoDB?
MongoDB offers schema flexibility and fast geolocation queries, making it ideal for rapidly changing restaurant/menu data.

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🧭 3. High-Level Design (HLD)

Client (Frontend)
      ⬇
[REST API Layer] — RestaurantController.java
      ⬇
[Service Layer] — RestaurantService.java
      ⬇
[Repository Services] — RestaurantRepositoryServiceImpl.java
      ⬇
[MongoDB] — RestaurantRepository, ItemRepository

• Controller Layer: Accepts HTTP requests and maps to appropriate service methods.
• Service Layer: Contains core business logic and orchestrates responses.
• Repository Layer: Interacts with MongoDB collections using Spring Data.

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🌟 4. Features

• 📍 Fetch nearby restaurants based on latitude and longitude.
• ⏰ Filters restaurants based on request time and open status.
• 🗃️ Uses fixture data for consistent testing and benchmarking.
• 🧪 Assessment-ready test suite using JUnit and Mockito.
• ⚠️ Custom exception handling with centralized logging.

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📡 5. API Usage

GET /qeats/v1/restaurants

Fetch restaurants near a location within a 5km radius.

✅ Sample Request:

GET /qeats/v1/restaurants?latitude=12.9281&longitude=77.6219&timestamp=2025-07-27T14:00:00.000Z

✅ Sample Response:

{
  "restaurants": [
    {
      "restaurantId": "1",
      "name": "Meghana Foods",
      "latitude": 12.9281,
      "longitude": 77.6219
    }
  ]
}

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💻 6. Local Setup

📦 Prerequisites

• Java 11+
• MongoDB running locally on localhost:27017
• Bash terminal (for setup scripts)

🔧 Steps

# Clone the repo
git clone https://github.com/sushantrishav90/ME_QEATS_V2.git
cd ME_QEATS_V2

# Setup MongoDB data
bash setup_mongo.sh

# Build the project
./gradlew clean build

# Run the application
./gradlew :qeatsbackend:bootRun

🌐 Access

API base URL: http://localhost:8080/qeats/v1/restaurants

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📌 7. Important Notes

• MongoDB fixture data is stored in resources/fixtures/.
• Restaurant availability is determined using current request timestamp.
• Time-based logic and geolocation accuracy are crucial for correct responses.
• Test files are located in:
  • src/test/java for general testing
  • src/test-assessment/java for CRIO assessment tests

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🚀 8. Scope for Future Development

• 🛒 Order placement and cart management
• 🧭 Real-time delivery tracking via WebSockets
• 🔍 Full-text restaurant and item search
• 📊 Analytics dashboard for admin usage
• 🧪 Integration tests and CI/CD pipelines
• 🐳 Docker containerization for microservice deployment
• ☁️ Cloud deployment on AWS/GCP with managed MongoDB

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Maintained by Shushant Rishav
For queries or contributions, feel free to open an issue or submit a PR.