labFusion/docs/progress.md

LabFusion Development Progress

Project Overview

LabFusion is a unified dashboard and integration hub for homelab services, built with a polyglot microservices architecture.

Architecture Decisions

• Modular Services: Each service is independently deployable
• Polyglot: Services can use different languages (Java, Python, Go, Node.js)
• Message Bus: Redis for inter-service communication
• Database: PostgreSQL for persistent data
• Containerization: Docker for all services

Completed Tasks ✅

Phase 1: Project Structure & Foundation

• Project Structure Setup (2024-11-09)

  • Created modular services/ directory structure
  • Moved from backend/ to services/ for better modularity
  • Each service has its own directory with independent Docker setup

• API Gateway Service (2024-11-09)

  • Java Spring Boot application
  • JPA entities for User, Dashboard, Widget, Event, DeviceState
  • REST controllers for dashboard management
  • PostgreSQL integration
  • Redis message bus support
  • JWT authentication framework

• Service Adapters (2024-11-09)

  • Python FastAPI application
  • Integration endpoints for Home Assistant, Frigate, Immich, n8n
  • Redis event publishing
  • Configurable service connections
  • Mock data for development

• Frontend Dashboard (2024-11-09)

  • React application with Ant Design
  • System metrics visualization
  • Service status monitoring
  • Settings configuration
  • Responsive design

• Infrastructure Setup (2024-11-09)

  • Docker Compose for production and development
  • PostgreSQL database configuration
  • Redis message bus setup
  • Environment configuration templates

• Documentation (2024-11-09)

  • Comprehensive README with setup instructions
  • Service-specific README files
  • Progress tracking document
  • Updated project structure documentation

• API Documentation Service (2024-11-09)

  • Unified Swagger/OpenAPI documentation service
  • Aggregates API specs from all services
  • Service health monitoring
  • Dynamic spec generation with service prefixing
  • Express.js service with Swagger UI integration

• Enhanced Service Adapters (2024-11-09)

  • Comprehensive OpenAPI documentation with Pydantic models
  • Detailed request/response schemas for all endpoints
  • Service-specific tags and descriptions
  • Enhanced error handling with proper HTTP status codes
  • Additional endpoints for better service integration

• Modular Service Adapters Refactoring (2024-11-09)

  • Refactored monolithic main.py into modular structure
  • Separated concerns: models, routes, services
  • Clean main.py (40 lines vs 424 lines)
  • Improved maintainability and team collaboration
  • Service-specific route organization

• Frontend Clean Code Refactoring (2024-11-09)

  • Applied clean code principles and React best practices
  • Refactored 155-line Dashboard component into focused components
  • Created reusable common components (StatusIcon, LoadingSpinner, ErrorBoundary)
  • Implemented component composition with dashboard-specific components
  • Added PropTypes for type safety and better development experience
  • Centralized constants and eliminated magic numbers/strings
  • Created utility functions for error handling and data formatting
  • Implemented proper separation of concerns (components, hooks, services, utils)
  • Added comprehensive error boundaries for graceful error handling
  • Improved code maintainability, testability, and readability

• Frontend Resilience & Offline Mode (2024-11-09)

  • Implemented offline mode for when backend services are unavailable
  • Added service status monitoring with real-time health checks
  • Created graceful error handling with user-friendly messages
  • Implemented fallback data and loading states
  • Added automatic retry mechanisms and service recovery detection
  • Created comprehensive error boundary system
  • Enhanced developer experience with clear error messages and debugging info

Current Status 🚧

Services Directory Structure

services/
├── api-gateway/          # Java Spring Boot (Port 8080) ✅
├── service-adapters/     # Python FastAPI (Port 8000) ✅
├── metrics-collector/    # Go service (Port 8081) 🚧
├── notification-service/ # Node.js service (Port 8082) 🚧
└── api-docs/             # API Documentation (Port 8083) ✅

Infrastructure

• Database: PostgreSQL (Port 5432) ✅
• Message Bus: Redis (Port 6379) ✅
• Frontend: React (Port 3000) ✅
• API Documentation: Unified Swagger UI (Port 8083) ✅
• Containerization: Docker Compose ✅
• CI/CD: Gitea Actions with specialized runners ✅
• Testing: Comprehensive test suites for all services ✅
• Security: Vulnerability scanning and code quality gates ✅

Documentation Status

• Main README: Comprehensive project overview ✅
• Service READMEs: Detailed documentation for each service ✅
• Clean Code Guides: Implementation details for all services ✅
• CI/CD Documentation: Complete pipeline and runner documentation ✅
• Architecture Documentation: Clean code principles and patterns ✅
• Troubleshooting Guides: Comprehensive problem-solving documentation ✅

Next Steps 🎯

Phase 2: Service Implementation

• Metrics Collector Service (Go)

  • Implement Docker API integration
  • Add Prometheus metrics collection
  • Create Redis publishing mechanism
  • Add configuration management

• Notification Service (Node.js/TypeScript)

  • Implement notification channels (email, webhook, push)
  • Create alert rule engine
  • Add notification preferences
  • Integrate with Redis for event processing

Phase 3: Real Service Integrations

• Home Assistant Integration

  • Implement actual HA API calls
  • Add WebSocket support for real-time updates
  • Create entity state synchronization

• Frigate Integration

  • Implement Frigate API integration
  • Add event processing and filtering
  • Create detection timeline visualization

• Immich Integration

  • Implement Immich API calls
  • Add photo metadata processing
  • Create asset management features

Phase 4: Advanced Features

• Real-time Updates

  • WebSocket implementation for live data
  • Event streaming from Redis
  • Frontend real-time UI updates

• Data Correlation Engine

  • Cross-service event correlation
  • Timeline analysis
  • Pattern detection

• Authentication & Security

  • JWT token implementation
  • User management
  • Role-based access control

Phase 5: Production Readiness

• Monitoring & Logging

  • Centralized logging
  • Health checks
  • Performance monitoring

• CI/CD Pipeline

  • GitHub Actions setup
  • Automated testing
  • Deployment automation

• Kubernetes Migration

  • Helm charts
  • Kubernetes manifests
  • Production deployment

Development Notes

Service Independence

Each service is designed to be:

• Independently deployable
• Language-agnostic
• Self-contained with its own dependencies
• Communicating via Redis message bus

Future Service Additions

The modular structure allows for easy addition of new services:

• Data Processing Service (Rust) - For heavy data processing
• ML Service (Python) - For machine learning features
• API Gateway v2 (Go) - For high-performance routing
• Cache Service (C++) - For high-performance caching

Phase 7: CI/CD Pipeline Setup

• Gitea Actions CI/CD (2024-12-09)

  • Complete CI/CD pipeline for polyglot microservices architecture
  • Individual service pipelines for Java, Python, Node.js, and React
  • Integration testing with PostgreSQL and Redis services
  • Local Docker builds for testing and validation
  • Security scanning with Trivy vulnerability scanner
  • Code quality gates with linting, testing, and coverage requirements
  • Comprehensive CI/CD documentation and configuration
  • Simplified pipelines focused on testing and validation

• Multi-Runner Infrastructure (2024-12-09)

  • Specialized runners for different workload types
  • Heavy runner for Java/Python workloads
  • Light runner for Node.js/Frontend workloads
  • Docker runner for integration tests
  • Security runner for vulnerability scanning
  • Docker Compose setup for runner management
  • Windows PowerShell and Linux/macOS management scripts
  • Comprehensive runner documentation and troubleshooting guides

• CI/CD Optimization (2024-12-09)

  • Optimized Docker images for faster builds
  • Specialized runner configurations
  • Cache optimization strategies
  • Performance monitoring and tuning
  • Docker rate limit solutions
  • Comprehensive optimization documentation

Technical Debt

• Add comprehensive error handling (Frontend)
• Implement proper logging across all services
• Add unit and integration tests (CI/CD pipelines)
• Create API documentation with OpenAPI/Swagger
• Add health check endpoints for all services
• Apply clean code principles (Frontend)
• Implement offline mode and resilience (Frontend)
• Set up CI/CD pipelines for automated testing and deployment
• Fix Maven command not found error in CI/CD pipelines (Added Maven wrapper)
• Fix Python formatting issues in CI/CD pipelines (Applied Black and isort formatting)
• Update flake8 line length limit to 150 characters for better readability
• Create JavaScript/Node.js tests for API docs service and frontend
• Fix Gitea Actions compatibility issues with artifact uploads
• Create Docker Compose setup for multiple Gitea runners
• Update all CI/CD pipelines with appropriate runner labels
• Fix "Cannot find: node in PATH" error by creating custom runner Docker images
• Organize runner-related files into dedicated runners/ folder
• Fix "usermod: group 'docker' does not exist" error in runner Dockerfiles
• Fix "registration file not found" error by adding automatic runner registration
• Refactor runners to use official gitea/act_runner:nightly image with individual config files
• Create comprehensive documentation for all services and CI/CD setup
• Implement clean code principles across all services
• Set up specialized runners for different workload types
• Optimize CI/CD performance with specialized Docker images
• Create management scripts for runner operations
• Implement comprehensive testing and security scanning

Resources

• Project Specifications
• Project Structure
• Main README