The ATM Handler is a robust system designed to manage and automate the electronic events of Automated Teller Machines (ATMs). It provides real-time monitoring through socket connections, ensuring that ATM transactions and error events are logged and handled efficiently. This project aims to optimize ATM performance and reliability, making it a crucial tool for institutions managing large ATM networks.
This handler is part of a broader set of tools within the ALX Software Engineering Programme, showcasing practical skills in real-time systems, event-driven programming, and Docker-based deployment.
Real-Time Socket Communication: The system listens for events directly from ATMs, providing up-to-the-minute updates on transactions and operations.
Comprehensive Event Logging: ATM events (e.g., withdrawals, deposits, balance inquiries, machine malfunctions) are captured and logged in a database for auditing and performance monitoring.
Automated Event Handling: Leveraging automation, the system can trigger specific responses to critical ATM events, such as system restarts, notifications to maintenance teams, or alerts to operators.
Docker Deployment: The application is containerized using Docker, allowing for simple deployment, scalability, and consistent environments across development and production.
Modular Design: Built with scalability in mind, the handler can be easily extended to integrate additional features such as predictive maintenance and data analytics.
The project follows a modular architecture that separates concerns, ensuring scalability and maintainability:
Socket Listener Module: Listens for incoming ATM events via secure sockets.
Event Logger: Captures and stores events in a structured format, allowing for easy querying and analysis.
Automated Response Module: Preconfigured rules and triggers for responding to specific events, such as maintenance or transaction errors.
Deployment Scripts: Docker scripts that handle containerization and deployment on various platforms.
Redis Integration: Used as a fast, in-memory data store to handle real-time event queues.
Programming Language: Python 3.9+
Containerization: Docker
In-Memory Storage: Redis
Database: PostgreSQL (or any SQL database of choice)
Socket Programming: Python’s socket library
Orchestration: Docker Compose for managing multi-container applications
Ensure you have the following installed:
Python 3.9 or higher
Docker and Docker Compose
Redis (optional, but recommended)
Step-by-Step Installation
Clone the repository:
git clone https://github.com/G-omar-H/ATM-handler.git
cd ATM-handlerSet up a virtual environment:
python3 -m venv my_atm_env
source my_atm_env/bin/activateInstall project dependencies:
pip install -r requirements.txtConfigure environment variables: Rename .env.example to .env and configure your settings:
REDIS_HOST: Redis server address
REDIS_PORT: Redis port number
DB_URI: Database connection string
Run the application with Docker Compose:
docker-compose up --buildCheck the logs:
docker logs -f atm-handlerStop the application:
docker-compose down💻 Usage
Start Monitoring ATMs: Once the system is running, it will automatically start listening for ATM events.
View Event Logs: Access logs via the logs directory or query the database directly for event history.
Automation: Set up triggers and alerts based on event types to automatically handle ATM malfunctions or errors.
🔄 Event Types
The system currently handles the following ATM events:
Cash Withdrawal Events
Deposit Events
Balance Inquiry
Error Handling (e.g., out of service, maintenance required)
Hardware Failure Reports
Future versions will include predictive analysis and machine learning integrations to preemptively identify potential failures. 🧑💻 Contributing
We welcome contributions from the open-source community! To contribute:
Fork the repository.
Create a feature branch: git checkout -b feature/new-feature.
Commit your changes: git commit -m "Add new feature".
Push to the branch: git push origin feature/new-feature.
Open a pull request.
Please ensure your contributions align with the project's coding standards and are well-documented. 🧪 Running Tests
To ensure code quality and functionality:
Install test dependencies:
pip install -r requirements-dev.txtRun the test suite:
pytest📅 Future Roadmap
Predictive Maintenance Module: Machine learning models to predict ATM failures before they occur.
ATM Fleet Management Dashboard: A web-based dashboard for visualizing ATM network health and event logs in real-time.
Support for Additional Event Types: Expanding the event types and actions handled by the system.
Integration with Bank APIs: To allow seamless communication between the ATM system and the bank’s backend for real-time transaction validation.
📜 License
This project is licensed under the MIT License. See the LICENSE file for details. 👥 Contributors
G-omar-H (Lead Developer)