A comprehensive platform for mainframe modernization, analysis, and management with frontend, backend services, and worker components.
The platform consists of three main components:
- Frontend (
mainframe_platform_frontend/) - React-based web interface - Services (
mainframe-services/) - FastAPI backend services - Workers (
mainframe-workers/) - Airflow-based data processing workers
graph TB subgraph "User Interface Layer" UI[React Frontend<br/>Port: 3000] UI --> |HTTP/WebSocket| API end subgraph "API & Services Layer" API[FastAPI Services<br/>Port: 8000] WS[WebSocket Manager] AUTH[Authentication Service] API --> AUTH API --> WS end subgraph "Processing Layer" AIRFLOW[Apache Airflow<br/>Port: 8080] WORKERS[Parser Workers] QUEUE[RabbitMQ<br/>Port: 15672] AIRFLOW --> WORKERS WORKERS --> QUEUE API --> QUEUE end subgraph "Data Storage Layer" MONGO[(MongoDB<br/>Primary Data)] REDIS[(Redis<br/>Cache & Queue)] NEBULA[(Nebula Graph<br/>Dependencies)] AZURE[Azure Blob Storage<br/>Files] end subgraph "External Services" OPENAI[Azure OpenAI<br/>AI Analysis] GIT[Git Repositories] end API --> MONGO API --> REDIS WORKERS --> MONGO WORKERS --> NEBULA WORKERS --> AZURE WORKERS --> OPENAI API --> GIT style UI fill:#e1f5fe style API fill:#f3e5f5 style AIRFLOW fill:#fff3e0 style MONGO fill:#e8f5e8 style OPENAI fill:#fff8e1 - Docker & Docker Compose - For running services and workers
- Node.js & Yarn - For frontend development
- Python 3.8+ - For backend services and workers
- Poetry - Python dependency management
cd mainframe_platform_frontend yarn install yarn startThe frontend will be available at http://localhost:3000
cd mainframe-services make install make devThis will:
- Install Python dependencies using Poetry
- Generate environment configuration
- Start the services stack using Docker Compose
Services will be available at http://localhost:8000
cd mainframe-workers make install make devThis will:
- Install Python dependencies using Poetry
- Setup RabbitMQ message queue
- Start Airflow development stack
Airflow UI will be available at http://localhost:8080
cd mainframe_platform_frontend yarn start # Start development server yarn build # Build for production yarn test # Run tests yarn check:all # Run linting and formatting checkscd mainframe-services make install # Install dependencies and setup environment make dev # Start development stack make dev-down # Stop development stack make dev-logs # View development logs make test # Run tests make clean # Clean Python cache filescd mainframe-workers make install # Install dependencies and setup RabbitMQ make dev # Start Airflow development stack make dev-down # Stop Airflow stack make dev-logs # View Airflow logs make dev-restart # Restart Airflow stack make dev-status # Show Airflow stack status make test # Run tests make lint # Run linting checks make format # Format code with black and isort make clean # Clean Python cache filesEach component has its own environment configuration:
- Frontend: Uses environment variables for API endpoints
- Services: Copy
env.dev.exampleto.envfor development - Workers: Copy
env.exampleto.envfor development
cd mainframe-services make dev-worker # Start only the worker make dev-worker-logs # View worker logs make service-logs # View service logscd mainframe-workers make run # Run worker directly (without Airflow)All components can be run using Docker Compose:
# Services cd mainframe-services docker-compose -f docker-compose.dev.yml up -d # Workers cd mainframe-workers docker-compose -f docker-compose-airflow-dev.yaml up -d# Services cd mainframe-services docker-compose up -d # Workers cd mainframe-workers docker-compose -f docker-compose-airflow.yaml up -d- Frontend:
http://localhost:3000 - Services API:
http://localhost:8000 - Airflow UI:
http://localhost:8080 - RabbitMQ Management:
http://localhost:15672
# Frontend tests cd mainframe_platform_frontend yarn test # Backend tests cd mainframe-services make test # Worker tests cd mainframe-workers make test# Stop all development stacks cd mainframe-services && make dev-down cd mainframe-workers && make dev-down # Clean Python cache cd mainframe-services && make clean cd mainframe-workers && make cleanmainframe/ ├── mainframe_platform_frontend/ # React frontend application ├── mainframe-services/ # FastAPI backend services └── mainframe-workers/ # Airflow-based data processing workers flowchart LR DEV[Developer] --> CODE[Code Changes] CODE --> TEST[Run Tests] TEST --> BUILD[Build & Package] BUILD --> DEPLOY[Deploy to Environment] subgraph "Frontend Development" FE_DEV[yarn start<br/>Port: 3000] FE_BUILD[yarn build] FE_TEST[yarn test] end subgraph "Services Development" SVC_DEV[make dev<br/>Port: 8000] SVC_BUILD[Docker Build] SVC_TEST[make test] end subgraph "Workers Development" WORK_DEV[make dev<br/>Airflow: 8080] WORK_BUILD[Docker Build] WORK_TEST[make test] end CODE --> FE_DEV CODE --> SVC_DEV CODE --> WORK_DEV TEST --> FE_TEST TEST --> SVC_TEST TEST --> WORK_TEST BUILD --> FE_BUILD BUILD --> SVC_BUILD BUILD --> WORK_BUILD style DEV fill:#4caf50 style FE_DEV fill:#61dafb style SVC_DEV fill:#009688 style WORK_DEV fill:#017cee - Follow the existing code style and formatting
- Run tests before submitting changes
- Use the provided Makefile commands for development tasks
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