🕹️ Game & Leaderboard Microservices Demo

Welcome to the Game & Leaderboard Microservices Demo! This project showcases a scalable microservices architecture using Node.js 20.0. The two services—Game and Leaderboard—leverage various tools and technologies for asynchronous communication, caching, and persistent storage. This README will guide you through the architecture, setup, and deployment. This project is intended solely as a demonstration and should not be used in production environments.

📖 Table of Contents

• Overview
• Technologies Used
• Microservices
  • Game Service
  • Leaderboard Service
• Setup
  • Prerequisites
• Testing
• CI/CD Pipeline
• License

🎮 Overview

This repository consists of two primary services:

1. Game Service - An API REST service that manages game sessions. It is connected to Redis for caching game data and communicates with an OpenAI Assistant API and RabbitMQ.
2. Leaderboard Service - An Apollo GraphQL service that receives messages from RabbitMQ to update game results in a PostgreSQL database.

Both services are containerized with Docker and designed to run on Kubernetes (K8s) with infrastructure provisioned via Terraform on Google Kubernetes Engine (GKE).

🛠 Technologies Used

• Node.js 20.0: JavaScript runtime for building scalable server-side applications
• Redis-om: In-memory data store for caching game sessions
• RabbitMQ: Message broker for asynchronous communication
• PostgreSQL 14.0: Relational database for leaderboard storage
• Sequelize ORM: Object-Relational Mapper for PostgreSQL
• Mocha & Supertest: Testing frameworks for API testing
• Docker: Containerization of services
• Kubernetes (K8s): Orchestrating microservices
• Terraform: Infrastructure as Code for provisioning GKE
• GitHub Actions: CI/CD pipeline for automated testing and deployment

🖥 Microservices

Game Service

• Endpoints:
  • POST /startNewGame: Initiates a new game session, storing game data in Redis.
  • POST /suggestSolution: Checks the player’s solution, validates game rules (game time, attempts, etc.), and fetches hints from the OpenAI Assistant API if needed. If the solution is correct, sends a success message to RabbitMQ and removes the session from Redis.
• Dependencies:
  • Redis: Caches the current game state for each user.
  • OpenAI API: Provides hints when the player’s suggestion isn’t correct.
  • RabbitMQ: Publishes success messages for the leaderboard update.

Leaderboard Service

• Technology: Apollo GraphQL server
• Functionality: Listens for RabbitMQ messages from the Game Service. Upon receiving a success message, updates the leaderboard in a PostgreSQL database using Sequelize ORM.
• GraphQL API: Exposes an endpoint for querying leaderboard data.

🔧 Setup

Prerequisites

• Node.js 20.0 installed
• Docker and Docker Compose installed
• Terraform installed
• Access to Google Cloud Platform for GKE

🧪 Testing

The Game Service includes unit and integration tests with Mocha and Supertest. Tests are located under game-service/tests.

📦 CI/CD Pipeline

The project includes a GitHub Actions workflow for continuous integration and deployment:

• Unit Testing: Automatically runs tests on each pull request.
• Docker Build & Push: Builds Docker images and pushes them to a container registry.
• Kubernetes Deployment: Deploys to GKE when changes are merged to the main branch.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.