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Step 10: Data Analytics and Statistics Service - Created comprehensive statistics service with real-time metrics collection - Implemented time-series data storage interface (InfluxDB compatible) - Added data aggregation and analytics endpoints - Integrated Redis caching for performance optimization - Made Kafka connection optional for resilience Step 11: Real-time Notification System - Built multi-channel notification service (Email, SMS, Push, In-App) - Implemented priority-based queue management with Redis - Created template engine for dynamic notifications - Added user preference management for personalized notifications - Integrated WebSocket server for real-time updates - Fixed pymongo/motor compatibility issues (motor 3.5.1) Testing: - Created comprehensive test suites for both services - Added integration test script to verify cross-service communication - All services passing health checks and functional tests 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
Site11 - Microservices Architecture
Overview
Microservices platform with Console as API Gateway orchestrating multiple domain services.
Quick Start
Start Services
# Start console service
docker-compose up -d console-backend
# Check status
curl http://localhost:8011/health
Available Endpoints
http://localhost:8011/- Root endpointhttp://localhost:8011/health- Health checkhttp://localhost:8011/api/status- System status
Architecture
- Console: API Gateway and orchestrator
- Services: Domain-specific microservices (users, oauth, images, etc.)
- Database: MongoDB for persistence
- Cache: Redis for caching and pub/sub
Development
See docs/PLAN.md for implementation roadmap and docs/PROGRESS.md for current status.