DevOps W07 In-Class Exercise Solution

This repository contains a full-stack canteen application with a SvelteKit client, Spring Boot server, and LLM recommendation service. It demonstrates modern web application architecture and DevOps practices.

Project Overview

This project includes:

• Client: SvelteKit with TypeScript, TailwindCSS, and reusable UI components for browsing canteen meals.
• Server: Spring Boot Java application with RESTful APIs, gRPC communication, and PostgreSQL integration.
• LLM Service: Python FastAPI service for generating meal recommendations using AI.
• Database: PostgreSQL for storing user preferences and application data.
• DevOps: Dockerized services, CI/CD pipelines, Helm charts, and production-ready deployment configurations.

Prerequisites

• Node.js (v22 or later)
• Java JDK 21+
• Python 3.x
• Gradle
• Docker and Docker Compose
• Git
• Kubernetes and Helm (for Kubernetes deployment)

Setup Instructions

Clone the Repository

git clone https://github.com/yourusername/w07-solution.git
cd w07-solution

Client Setup

1. Navigate to the client directory:

   cd client

2. Install dependencies:

   npm install

Server Setup

1. Navigate to the server directory:

   cd server

2. Build the project:

   ./gradlew build

LLM Service Setup

1. Navigate to the llm directory:

   cd llm

2. Install dependencies:

   python3 -m venv .venv
   source .venv/bin/activate
   pip3 install -r requirements.txt

Running the Application

Start the Database

docker compose up database -d

Start the Client

cd client
npm run dev

The client will be available at http://localhost:3000.

Start the Server

cd server
./gradlew bootRun

The server API will be available at http://localhost:8080.

Start the LLM Service

cd llm
python main.py

The LLM service will be available at http://localhost:5000.

Development Workflow

Client Development

• Built with SvelteKit and TypeScript for a modern, reactive UI.
• TailwindCSS for styling.
• Components and routes are organized in the src directory.
• Features meal browsing, favoriting, and user preferences.

Server Development

• Built with Spring Boot for scalable and maintainable server services.
• Includes gRPC communication with the LLM service.
• PostgreSQL integration for user preferences storage.
• RESTful APIs for canteen data and user management.
• Gradle is used for dependency management and building.
• Source code is in the src/main/java directory.
• Tests are in the src/test/java directory.

LLM Service Development

• Built with FastAPI for AI-powered meal recommendations.
• Integrates with external LLM APIs for generating personalized suggestions.
• Source code is in the llm directory.

Building for Production

Client Build

cd client
npm run build

Server Build

cd server
./gradlew clean build

Dockerized Deployment

The project includes Docker configurations for containerized deployment.

Build and Run with Docker Compose

1. Build and start all services:

   docker compose up --build

2. Access the application:
   • Client: http://localhost:3000
   • Server: http://localhost:8080
   • LLM Service: http://localhost:5000
   • Database: PostgreSQL on port 5432

Kubernetes Deployment

The project includes Helm charts for Kubernetes deployment.

Deploy with Helm

1. Update the tumid value in helm/canteen-app/values.yaml:

   tumid: your-tum-id

2. Install the Helm chart:

   helm install canteen ./helm/canteen-app

CI/CD Pipeline

The project includes GitHub Actions workflows for:

• Building Docker Images: Automatically builds and pushes Docker images to GitHub Container Registry.
• Deploying Docker Images: Deploys the application to a production environment using Docker Compose.

Project Structure

├── client/                  # SvelteKit client
│   ├── src/                 # Source code
│   ├── static/              # Static assets
│   └── package.json         # Client dependencies
│
├── server/                  # Spring Boot server
│   ├── src/                 # Source code including gRPC services
│   ├── build.gradle         # Gradle build file
│   └── Dockerfile           # Server Dockerfile
│
├── llm/                     # Python LLM service
│   ├── main.py              # FastAPI application
│   ├── requirements.txt     # Python dependencies
│   └── Dockerfile           # LLM service Dockerfile
│
├── docs/                    # API documentation (Bruno collection)
├── compose.yml              # Docker Compose for local development
└── .github/workflows/       # CI/CD workflows

API Documentation

API documentation is available in the docs/CanteenApp Bruno directory as a Bruno collection for testing endpoints.

License

This project is licensed under the MIT License.