Python Implementation

[davideme]

Overview

Implement the Lamp Control API in Python with all required interfaces (REST, GraphQL, gRPC) and database support.

Tasks

• Set up basic Python project structure
• Implement REST API with FastAPI and OpenAPI 3.0
• Implement GraphQL API with Strawberry or Ariadne
• Implement gRPC API
• Implement database connectors for MySQL, PostgreSQL, and MongoDB
• Write comprehensive tests for all components
• Document implementation details and setup instructions

Technical Details

• Use the most efficient connection systems for Python
• Implement using popular ORMs/ODMs (SQLAlchemy, pymongo, etc.)
• Follow the API specifications in the docs directory
• Include versioning in API paths (v1)

Acceptance Criteria

• All three API interfaces (REST, GraphQL, gRPC) are fully implemented
• Complete database support for MySQL, PostgreSQL, and MongoDB
• Tests covering all functionality with >80% coverage
• README with clear setup and usage instructions
• Adherence to project coding standards
• Lines of code metrics are documented

Notes

• Focus on idiomatic Python code (PEP 8 compliance)
• Prioritize code clarity and maintainability
• Use type hints

[davideme]

Detailed Implementation Plan

1. Project Setup and Structure

• Initialize Python project with poetry/pipenv
• Create directory structure following feature-based organization
  • /src/api - API implementations
  • /src/core - Core business logic
  • /src/db - Database implementations
  • /src/models - Data models
  • /tests - Test suites
• Set up pre-commit hooks for linting and formatting
• Configure mypy for static type checking
• Set up logging configuration with structlog
• Create initial README.md with project overview

2. Database Layer Implementation

• Define base database interfaces and models
• Implement SQLAlchemy models and migrations
  • Create base model with UUID, timestamps, and soft delete
  • Implement Lamp model and related entities
  • Set up Alembic for migrations
• Implement PostgreSQL connector
  • Connection pooling configuration
  • Repository pattern implementation
• Implement MySQL connector
  • Connection pooling configuration
  • Repository pattern implementation
• Implement MongoDB connector with Motor
  • Define ODM models
  • Implement async repository pattern
• Add database monitoring and metrics

3. Core Business Logic

• Implement lamp control service layer
  • Define service interfaces
  • Implement lamp state management
  • Add validation logic
• Implement caching layer with Redis
• Add business event logging
• Implement error handling and custom exceptions

4. REST API Implementation (FastAPI)

• Set up FastAPI application structure
• Implement OpenAPI 3.0 specifications
• Create v1 API endpoints
  • GET /api/v1/lamps
  • GET /api/v1/lamps/{id}
  • POST /api/v1/lamps
  • PUT /api/v1/lamps/{id}
  • DELETE /api/v1/lamps/{id}
  • POST /api/v1/lamps/{id}/toggle
• Add request/response validation
• Implement authentication middleware
• Add rate limiting
• Set up API documentation with Swagger UI

5. GraphQL API Implementation (Strawberry)

• Set up Strawberry GraphQL application
• Define GraphQL schema
  • Lamp type and inputs
  • Queries
  • Mutations
  • Subscriptions for state changes
• Implement resolvers
• Add DataLoader for efficient querying
• Set up GraphQL playground
• Add query complexity analysis
• Implement field-level permissions

6. gRPC API Implementation

• Define protocol buffers
  • Lamp service definition
  • Message types
  • Service methods
• Generate gRPC code
• Implement service handlers
• Add streaming support for state changes
• Set up gRPC reflection
• Implement interceptors for logging and metrics

7. Testing

• Set up pytest configuration
• Implement unit tests
  • Core business logic
  • Service layer
  • Database repositories
• Implement integration tests
  • REST API endpoints
  • GraphQL resolvers
  • gRPC services
• Add performance tests
  • API response times
  • Database operations
• Set up test coverage reporting
• Add load testing scripts

8. Security Implementation

• Implement authentication system
  • JWT token handling
  • Role-based access control
• Add input validation and sanitization
• Implement rate limiting
• Set up security headers
• Add audit logging
• Implement secure configuration management

9. Observability

• Set up structured logging
  • Request/response logging
  • Error logging
  • Business event logging
• Implement metrics collection
  • API metrics
  • Database metrics
  • Business metrics
• Add distributed tracing
• Create monitoring dashboards
• Set up alerting rules

10. Documentation

• Complete API documentation
  • REST API endpoints
  • GraphQL schema
  • gRPC services
• Add setup instructions
• Create development guide
• Document database schema
• Add performance considerations
• Include deployment guide

11. Quality Assurance

• Set up CI/CD pipeline
  • Linting and type checking
  • Test execution
  • Coverage reporting
  • Security scanning
• Implement code quality gates
• Add performance benchmarks
• Create release checklist
• Set up automated dependency updates

12. Deployment

• Create Dockerfile
• Set up docker-compose for local development
• Add Kubernetes manifests
• Create deployment scripts
• Document scaling considerations

Each task should follow the project's coding standards and development rules. Regular commits should be made following the conventional commits format, and all changes should go through code review before merging.

[davideme]

Python Version Selection

After careful consideration, we will use Python 3.12 for this implementation. Here's the rationale:

1. Support Timeline

   • Released: October 2023
   • Security support until: October 2028
   • Provides a good balance of modern features and long-term stability

2. Key Features Beneficial for This Project

   • Improved performance (~5% faster than Python 3.11)
   • Enhanced error messages and debugging capabilities
   • Modern type annotation syntax (PEP 695)
   • Improved f-string parsing
   • Buffer protocol support in Python code
   • New debugging/profiling API

3. Development Benefits

   • Mature tooling support
   • Wide library compatibility
   • Active security maintenance
   • Strong type checking capabilities

4. Version Requirements

   • Minimum version: Python 3.12.9
   • Recommended: Latest patch version (currently 3.12.10)

5. Development Environment Setup

   # Create virtual environment
   python3.12 -m venv venv
   
   # Activate virtual environment
   source venv/bin/activate  # Unix/macOS
   # or
   .\\venv\\Scripts\\activate  # Windows
   
   # Verify Python version
   python --version  # Should show Python 3.12.x

This version selection provides a good balance between modern features, performance, and long-term maintainability while ensuring we have access to the latest security updates for the foreseeable future.