This repository contains a microservice project developed to manage and send notifications efficiently and scalably. Utilizing best software engineering practices, this service is designed with a focus on robustness, testability, and observability.

1. Microservices Architecture: The project follows a microservices architecture, allowing each component to be independent and scalable, facilitating system maintenance and evolution.

2. RabbitMQ for Request Queueing: To ensure scalability and low latency, RabbitMQ is used to queue notification requests, ensuring a smooth processing flow even during peak loads.

3. External API: An external API has been developed to allow easy and flexible integration with other systems, facilitating the sending of notification requests to the microservice.

4. MongoDB as Database: MongoDB is used as the database to store relevant information about notifications, ensuring efficient persistence and retrieval of data.

5. Rich Domain and DDD Practices: The service is implemented using Rich Domain concepts and following Domain-Driven Design (DDD) practices, resulting in a more cohesive and business-oriented design.

6. Comprehensive Testing: The code is covered by a comprehensive suite of automated tests, ensuring the quality and reliability of the system. Unit, integration, and end-to-end tests are employed to validate the microservice's behavior in different scenarios.

7. Observability: The application features observability resources, including appropriate metrics and logging, ensuring that the performance and state of the system can be effectively monitored.

8. Grafana for Monitoring: We use Grafana to visualize and monitor important metrics related to RabbitMQ, the API, and Docker Compose containers, providing valuable insights into the system's performance and health.

9. Nginx for Load Balancing:

   • Nginx is utilized for load balancing, distributing incoming traffic across multiple instances of the microservice to improve performance and reliability.

1. Clone this repository.
2. Make sure you have Docker and Docker Compose installed on your machine.
3. Navigate to the project's root directory and run docker-compose up to start the application.
4. Access the external API interface through the URL provided by the documentation.
5. Explore metrics and visualizations in Grafana to monitor the system in real-time.

Contributions are welcome! Feel free to propose improvements, fix bugs, or add new features. Just open an issue or submit a pull request, and we'll be happy to review.

This project is licensed under the MIT License.

For more information or support, contact Fernando Areias.