NextMeal Backend Repository

At NextMeal, we are building an intelligent dining platform that seamlessly integrates a full suite of technologies: secure user authentication via Keycloak (SSO and 2FA), microservices built with Python Flask, Node.js, and Spring Boot (Java), persistent data storage using PostgreSQL (RDS), DynamoDB, and OpenSearch for vector embeddings, Redis (Elasticache) for caching, SQS for messaging, S3 for media storage, and OpenAI for AI-driven recommendations. All components are containerized with Docker, stored in AWS ECR, and orchestrated on AWS ECS, providing a scalable and maintainable ecosystem that dynamically adapts to user preferences and enhances their dining experience.

Table of Contents

• Overview
• Microservices
  • AI Assistant Service
  • User Auth Service
  • Reservation Handler Service
  • Reservation Queue Service
  • Restaurant Service
  • Review Management Service
• Technologies Used
• Setup Instructions
• Deployment
• Contributing

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Overview

This backend system is designed as a collection of six microservices that handle various aspects of the NextMeal platform. These services include user authentication, restaurant information, reservation handling, review management, queue processing, and AI-powered assistance.

All microservices are containerized using Docker, their images are pushed to AWS ECR (Elastic Container Registry), and they are deployed on AWS ECS (Elastic Container Service). Databases and other AWS services such as S3, SQS, Elasticache, and OpenSearch are fully integrated.

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Microservices

1. AI Assistant Service

• Backend Framework: Python Flask
• Database: OpenSearch (AWS)
• External API: OpenAI API
• Description: Provides AI-driven assistance for user queries, such as recommending meals, answering FAQs, or providing guidance.
• Recent Changes: Initial implementation with AI assistant logic added.

2. User Auth Service

• Backend Framework: Node.js
• Database: PostgreSQL
• Description: Handles user authentication, authorization, and session management.
• Recent Changes: Service added with authentication endpoints.

3. Reservation Handler Service

• Backend Framework: Spring Boot
• Database: PostgreSQL (RDS in AWS)
• Queue: Amazon SQS
• Description: Manages restaurant reservations, integrates with Reservation Queue Service for real-time updates.
• Recent Changes: Added CORS config and updated request handling logic.

4. Reservation Queue Service

• Backend Framework: Spring Boot
• Database: PostgreSQL (RDS in AWS)
• Queue: Amazon SQS
• Description: Manages reservation queues for real-time status updates.
• Recent Changes: Configured CORS and updated request logic.

5. Restaurant Service

• Backend Framework: Python Flask
• Database: PostgreSQL (primary), Redis (AWS Elasticache)
• Storage: Amazon S3 (for images)
• Description: Manages restaurant info, menus, locations, availability. Uses Redis caching and S3 for images.
• Recent Changes: Added Redis caching and S3 integration.

6. Review Management Service

• Backend Framework: Python Flask
• Database: DynamoDB (AWS)
• Description: Handles user reviews (CRUD operations).
• Recent Changes: Fixed minor issues and improved stability.

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Technologies Used

• Backend: Python Flask, Spring Boot, Node.js
• Databases: PostgreSQL (RDS), DynamoDB, OpenSearch
• Caching: Redis (Elasticache)
• Queue: Amazon SQS
• Storage: Amazon S3
• External APIs: OpenAI
• Authentication: Keycloak (SSO, 2FA)
• Containerization: Docker
• Registry: AWS ECR
• Orchestration: AWS ECS
• Monitoring: AWS CloudWatch

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Setup Instructions

Prerequisites

• Python 3.9+
• Node.js
• Spring Boot (Java 17+)
• Redis
• PostgreSQL
• AWS CLI configured for DynamoDB, OpenSearch
• Docker

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Deployment

1. Clone the repository: git clone <repository-url> cd <repository-folder>

2. Preparing Microservices:

   • Navigate to each microservice directory:
     cd <microservice-name>

   • Install dependencies:
     For Python (Flask): pip3 install -r requirements.txt
     For Node.js: npm install
     For Spring Boot (Java 21): Build with Maven/Gradle in IDE.

   • Configure environment variables in .env files:

     • Database endpoints
     • Redis connection strings
     • SQS queue URLs
     • API keys (e.g., OpenAI)

   • Run microservices locally:
     Python (Flask): python3 app.py
     Node.js: npm start
     Spring Boot (Java 17): mvn spring-boot:run

   • Containerize and push to AWS ECR:
     Create Docker images: docker build -t <image-name> .
     Authenticate and push images:
     aws ecr get-login-password --region <region> | docker login --username AWS --password-stdin <account-id>.dkr.ecr.<region>.amazonaws.com
docker tag <image-name>:latest <account-id>.dkr.ecr.<region>.amazonaws.com/<repository-name>
docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<repository-name>

   • Deploy to AWS ECS:

     • Create task definitions (AWS Management Console or CLI).
     • Deploy tasks to an ECS cluster.
     • Use a load balancer for public-facing services if needed.

   • Additional Resources:

     • Databases: PostgreSQL (RDS), DynamoDB
     • Caching: Redis (Elasticache)
     • Messaging: SQS
     • Storage: S3
     • Search: OpenSearch
     • Monitoring: CloudWatch

   • CI/CD Integration:

     • Use AWS CodePipeline and CodeBuild for automated deployments.
     • Add tests for all services before deployment.

   • Scaling and Monitoring:

     • Enable ECS auto-scaling.
     • Monitor via CloudWatch.

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Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch for your feature/fix.
3. Submit a pull request with a detailed description.