Fastlytics Backend API

This directory contains the backend components for the Fastlytics application.

Components

• main.py: A FastAPI application that serves Formula 1 data. It reads pre-processed data from the data_cache directory and can also fetch live data using the FastF1 library.
• processor.py: A Python script that uses the FastF1 library to fetch historical F1 data (schedules, results, standings, lap times, etc.), processes it, and saves it into JSON files within the data_cache directory. This pre-processing step speeds up API responses for historical data.
• data_cache/: Directory where the processor.py script stores the processed JSON data files, organized by year.
• cache/: Directory used by the FastF1 library to cache raw API responses, reducing redundant external requests.
• .env: Environment variables file. Should contain FASTLYTICS_API_KEY for securing the API endpoints and optionally FRONTEND_URL for CORS configuration.
• requirements.txt: (Optional - If you create one) Lists Python dependencies.

Setup & Usage

1. Dependencies: Ensure you have Python installed. It's recommended to use a virtual environment. Install dependencies (primarily fastapi, uvicorn, fastf1, pandas, numpy, python-dotenv):

   # Install core dependencies + FastF1 with plotting extras
   pip install fastapi uvicorn "fastf1[full]" pandas numpy python-dotenv requests requests_cache
   # Optional: Install Gunicorn for production deployment
   pip install gunicorn
   # Or install from requirements.txt if provided
   # pip install -r requirements.txt

2. Environment Variables: Create a .env file in this directory and add your desired FASTLYTICS_API_KEY.

   FASTLYTICS_API_KEY=your_secret_api_key_here
   FRONTEND_URL=http://localhost:5173 # Or your frontend's URL
   

3. Run Data Processor: Execute the processor script to fetch and cache historical data. This can take some time initially.

   python processor.py

4. Run API Server: Start the FastAPI server using Uvicorn.

   # For development (with auto-reload)
   uvicorn main:app --host 0.0.0.0 --port 8000 --reload

   The API will then be accessible, typically at http://localhost:8000.

   Alternative (Production with Gunicorn): For production deployments, using Gunicorn with Uvicorn workers is often recommended for better process management.

   # Example: Run with 4 worker processes
   gunicorn -k uvicorn.workers.UvicornWorker main:app --bind 0.0.0.0:8000 -w 4

   • -k uvicorn.workers.UvicornWorker: Specifies the Uvicorn worker class for ASGI compatibility.
   • --bind 0.0.0.0:8000: Sets the address and port to listen on.
   • -w 4: (Optional) Specifies the number of worker processes (adjust based on your server resources).