SportPulse is a real-time sports score aggregator built with high-performance C++ and designed following modern architecture and SRE best practices. The system consumes data from the free TheSportsDB API, processes live scores and events with sub-500ms latency, and exposes this information through a Command Line Interface (CLI) – with plans for a REST API and interactive web dashboard. Containerized for cloud deployment and integrated with Prometheus/Grafana for monitoring, SportPulse is built to showcase excellence in C++ performance, full-stack development, and site reliability engineering (SRE).
- Overview
- Key Features
- Architecture
- Detailed Component Breakdown
- Getting Started
- Usage
- Deployment
- Monitoring & Observability
- Automation & Resilience
- Future Roadmap
- Contributing
- License
- Contact
SportPulse is designed to deliver a robust demonstration of:
- C++ Performance: High-efficiency processing of live sports data.
- Full-Stack Development: Starting with a CLI and planning for a RESTful API and interactive dashboard.
- Architecture: A modular, scalable, and maintainable design.
- SRE Practices: Integrated monitoring, automated deployments, observability, and resilience testing.
The project not only provides real-time sports analytics for fans and media partners but also serves as a flagship portfolio piece for employers and tech enthusiasts.
-
Real-Time Data Ingestion:
Fetches live scores and events from TheSportsDB, updating every 5 seconds. -
High-Performance Processing:
Utilizes multithreading in C++ (usingstd::thread) to achieve sub-500ms processing latency. -
Modular Architecture:
Separates core processing, API integration, and presentation layers for clarity and future enhancements. -
Containerization & Cloud Deployment:
Dockerized for consistent environments and designed for scalable cloud deployments (AWS ECS, DigitalOcean, or Kubernetes). -
Robust Monitoring & SRE Practices:
Uses Prometheus for metrics collection, Grafana for dashboards, and automated scripts for health checks and recovery. -
Automated CI/CD Pipeline:
Implements a GitHub Actions pipeline to automate builds, tests, and deployments.
graph TD;
A["User"] --> B["Load Balancer (AWS ALB or Similar)"];
B --> C["SportPulse Containers (ECS/Kubernetes)"];
C --> D["C++ Core Processing Module"];
D --> E["Real-Time Data Processing"];
D --> F["Statistics Calculation"];
E --> G["API Integration (TheSportsDB)"];
C --> H["Monitoring & Observability"];
H --> I["Prometheus: Metrics Collection"];
H --> J["Grafana: Dashboards & Alerts"];
C --> K["Automation & CI/CD"];
K --> L["Python Health Check Scripts"];
K --> M["GitHub Actions Pipeline"];
Responsibility:
The C++ core is responsible for the ingestion and processing of live sports data.
Key Features:
- Utilizes libcurl for HTTP requests.
- Parses JSON responses using nlohmann/json.
- Implements multithreading (
std::thread) for parallel processing.
Responsibility:
Handles communication with external services (TheSportsDB), including:
- HTTP calls
- Error handling
- Data validation
CLI Interface:
- Provides a Command Line Interface (CLI) for immediate interaction with live scores and statistics.
Future Expansion:
- A planned RESTful API (e.g., GET
/live-scores) and an interactive web dashboard to increase accessibility and monetization potential.
Docker:
- The application is containerized to ensure consistent builds and deployment.
Cloud Deployment:
- Deployed on cloud platforms (AWS ECS, DigitalOcean, or Kubernetes) with load balancing and scalability in mind.
Prometheus:
- Gathers essential metrics such as latency, throughput, and resource usage.
Grafana:
- Visualizes these metrics in real-time dashboards and sets up alerts for critical thresholds.
CI/CD Pipeline:
- Uses GitHub Actions for automated builds, tests, and deployments.
Health Checks & Auto-Recovery:
- Python scripts perform periodic health checks and trigger automatic restarts if failures occur.
Resilience Testing:
- Simulated failure scenarios help validate auto-recovery mechanisms and maintain robust SLIs/SLOs.
graph TD;
A[C++ Core] --> B[API Integration];
A --> C[Presentation Layer];
A --> D[Containerization & Cloud Infrastructure];
A --> E[Monitoring & Observability];
A --> F[Automation & Resilience];
Development Tools:
- C++ compiler (e.g., g++)
- CMake for build automation
- libcurl and nlohmann/json libraries
- Docker for containerization
- Python 3 for automation scripts
Cloud Account:
- Access to AWS, DigitalOcean, or another cloud provider for deployment.
- Clone the Repository:
git clone https://github.com/dionisioedu/SportPulse.git
cd SportPulse- Install Dependencies: Ensure that libcurl and the JSON library are installed on your system. For example, on Ubuntu:
sudo apt-get update
sudo apt-get install libcurl4-openssl-dev
# For nlohmann/json, install via package manager or add it as a submodule- Build the Project:
mkdir build && cd build
cmake ..
makeAfter building the project, run the CLI application to start fetching and processing live sports data:
./SportPulseThe CLI will output live scores and calculated statistics (e.g., average goals per game) in real time.
- Build the Docker Image: Create a Dockerfile with the following sample content:
# Use an official Ubuntu base image
FROM ubuntu:20.04
# Install necessary packages
RUN apt-get update && apt-get install -y \
build-essential \
libcurl4-openssl-dev \
cmake \
git
# Copy the source code into the container
COPY . /app
WORKDIR /app
# Build the application
RUN mkdir build && cd build && cmake .. && make
# Set the entry point to run the application
ENTRYPOINT ["./build/SportPulse"]- Build the Docker image:
docker build -t sportpulse:latest .- Push to Docker Hub:
docker tag sportpulse:latest yourdockerhubusername/sportpulse:latest
docker push yourdockerhubusername/sportpulse:latestDeploy the Docker container to your preferred cloud provider (e.g., AWS ECS, DigitalOcean, or Kubernetes). Ensure the deployment includes load balancing, health checks, and auto-scaling configurations.
Prometheus:
- Configure Prometheus to scrape metrics from SportPulse.
- Expose endpoints in your application that provide key performance data.
Grafana:
- Create dashboards to visualize metrics such as API response latency, update frequency, and system resource usage.
Alerting:
- Set up alert rules to notify on critical events (e.g., API downtime, high latency).
CI/CD Pipeline:
- Utilize GitHub Actions to automate the build, test, and deployment processes. This ensures every commit is verified and deployed seamlessly.
Health Check Scripts:
- Implement Python scripts to perform periodic health checks and restart the application if necessary.
- These scripts are integrated with the CI/CD pipeline and cloud deployment.
Resilience Testing:
- Regularly simulate failures (e.g., API downtime, load spikes) to test and improve the system's auto-recovery capabilities.
- Document SLIs/SLOs based on these tests.
-
REST API & Web Dashboard: Develop a RESTful API endpoint and a modern web dashboard to provide enhanced user interaction and analytics.
-
Advanced Analytics: Add features for detailed statistical analysis and predictive insights on sports events.
-
Mobile Application: Consider developing a mobile app to allow users to access live scores on the go.
-
Monetization: Explore integration of ad networks or premium subscription models to monetize the platform.
Contributions are welcome!
To contribute:
Fork the repository. Create a feature branch. Make your changes and ensure they follow our coding standards. Submit a pull request for review. For issues or feature requests, please use the GitHub issues tracker.
This project is licensed under the MIT License – see the LICENSE file for details.
For inquiries, collaboration, or further information, please reach out:
YouTube Channel: Dionisio Developer
Twitter: @dionisiodev
Email: [email protected]
GitHub: @dionisioedu