Andrew K. Choi
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339 lines
12 KiB
Markdown
339 lines
12 KiB
Markdown
# Architecture Documentation - Women's Safety App
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## Overview
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This document describes the microservices architecture of the Women's Safety App backend, designed to handle millions of users with high availability, scalability, and performance.
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## System Architecture
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```
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┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
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│ Mobile App │ │ Web Client │ │ Admin Panel │
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└─────────────────┘ └──────────────────┘ └─────────────────┘
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│ │ │
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└───────────────────────┼───────────────────────┘
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│
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┌───────────────────────────┐
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│ Load Balancer │
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│ (NGINX/HAProxy) │
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└───────────────────────────┘
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│
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┌───────────────────────────┐
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│ API Gateway │
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│ (Rate Limiting, │
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│ Authentication, │
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│ Request Routing) │
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└───────────────────────────┘
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│
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┌─────────────┬──────────────┼──────────────┬─────────────┬─────────────┐
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│ │ │ │ │ │
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┌─────────┐ ┌─────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐
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│ User │ │Emergency│ │ Location │ │ Calendar │ │Notification │ │ Nutrition │
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│Service │ │Service │ │ Service │ │ Service │ │ Service │ │ Service │
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│:8001 │ │:8002 │ │ :8003 │ │ :8004 │ │ :8005 │ │ :8006 │
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└─────────┘ └─────────┘ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘
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│ │ │ │ │ │
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└─────────────┼──────────────┼──────────────┼─────────────┼─────────────┘
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│ │ │ │
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┌────────────────────────────────────────────────┐
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│ Message Bus │
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│ (Kafka/RabbitMQ) │
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└────────────────────────────────────────────────┘
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│
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┌─────────────┬──────────────┼──────────────┬─────────────┐
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│ │ │ │ │
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┌─────────┐ ┌─────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐
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│PostgreSQL│ │ Redis │ │ Kafka │ │Prometheus │ │ Grafana │
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│(Database)│ │(Cache) │ │(Events) │ │(Monitoring) │ │(Dashboards) │
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└─────────┘ └─────────┘ └─────────────┘ └─────────────┘ └─────────────┘
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```
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## Microservices Details
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### 1. User Service (Port 8001)
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**Responsibilities:**
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- User registration and authentication
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- Profile management
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- JWT token generation and validation
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- User settings and preferences
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**Database Tables:**
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- `users` - User profiles and authentication data
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**Key Features:**
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- JWT-based authentication
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- Password hashing with bcrypt
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- Email/phone verification
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- Profile picture upload
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- Privacy settings
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### 2. Emergency Service (Port 8002)
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**Responsibilities:**
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- Emergency alert creation and management
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- SOS signal processing
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- Emergency response coordination
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- Alert resolution tracking
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**Database Tables:**
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- `emergency_alerts` - Emergency incidents
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- `emergency_responses` - User responses to alerts
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**Key Features:**
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- Real-time alert broadcasting
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- Geolocation-based alert targeting
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- Response tracking and statistics
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- Integration with external emergency services
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### 3. Location Service (Port 8003)
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**Responsibilities:**
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- User location tracking
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- Geospatial queries
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- Proximity calculations
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- Location history management
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**Database Tables:**
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- `user_locations` - Current user locations
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- `location_history` - Historical location data (partitioned)
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**Key Features:**
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- Efficient geospatial indexing
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- Privacy-preserving location sharing
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- Location-based user discovery
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- Geographic data anonymization
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### 4. Calendar Service (Port 8004)
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**Responsibilities:**
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- Women's health calendar
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- Menstrual cycle tracking
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- Health insights generation
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- Reminder notifications
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**Database Tables:**
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- `calendar_entries` - Daily health entries
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- `cycle_data` - Menstrual cycle information
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- `health_insights` - AI-generated insights
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**Key Features:**
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- Cycle prediction algorithms
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- Health pattern analysis
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- Personalized insights
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- Data export for healthcare providers
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### 5. Notification Service (Port 8005)
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**Responsibilities:**
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- Push notification delivery
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- Device token management
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- Notification templates
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- Delivery tracking
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**Technologies:**
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- Firebase Cloud Messaging (FCM)
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- Apple Push Notification Service (APNs)
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- WebSocket for real-time notifications
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**Key Features:**
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- Multi-platform push notifications
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- Notification preferences
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- Delivery confirmation
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- Batch notification processing
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### 6. API Gateway (Port 8000)
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**Responsibilities:**
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- Request routing and load balancing
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- Authentication and authorization
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- Rate limiting and throttling
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- Request/response transformation
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- API versioning
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**Key Features:**
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- Circuit breaker pattern
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- Request caching
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- API analytics
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- CORS handling
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- SSL termination
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## Data Storage Strategy
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### PostgreSQL - Primary Database
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- **Partitioning Strategy:**
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- Location history partitioned by date (monthly)
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- Emergency alerts partitioned by geographic region
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- Calendar entries partitioned by user ID ranges
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- **Replication:**
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- Master-slave replication for read scaling
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- Geographic replicas for global distribution
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### Redis - Caching Layer
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- **Cache Types:**
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- Session storage (JWT tokens)
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- User location cache (5-minute TTL)
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- Frequently accessed user profiles
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- Emergency alert counters
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- **Cache Patterns:**
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- Write-through for user profiles
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- Write-behind for analytics data
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- Cache-aside for location data
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### Message Queue (Kafka)
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- **Topics:**
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- `emergency-alerts` - New emergency alerts
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- `user-locations` - Location updates
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- `notifications` - Push notification requests
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- `analytics-events` - User behavior tracking
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## Scalability Considerations
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### Horizontal Scaling
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- Each microservice can be scaled independently
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- Load balancing with consistent hashing
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- Database sharding by geographic region
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- Auto-scaling based on CPU/memory metrics
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### Performance Optimizations
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- Database connection pooling
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- Query optimization with proper indexing
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- Async/await for I/O operations
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- Response compression
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- CDN for static assets
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### High Availability
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- Multi-zone deployment
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- Health checks and auto-recovery
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- Circuit breakers for external dependencies
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- Graceful degradation strategies
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## Security Architecture
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### Authentication & Authorization
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- JWT tokens with short expiration
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- Refresh token rotation
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- Multi-factor authentication support
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- OAuth2/OIDC integration ready
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### Data Protection
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- Encryption at rest (AES-256)
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- Encryption in transit (TLS 1.3)
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- PII data anonymization
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- GDPR compliance features
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### Network Security
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- API rate limiting per user/IP
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- DDoS protection
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- Input validation and sanitization
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- SQL injection prevention
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- CORS policy enforcement
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## Monitoring & Observability
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### Metrics (Prometheus)
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- Service health metrics
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- Request rate and latency
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- Error rates and types
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- Database connection pool status
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- Cache hit/miss ratios
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### Logging
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- Structured logging (JSON format)
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- Centralized log aggregation
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- Log levels and filtering
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- Sensitive data masking
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### Alerting
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- Service downtime alerts
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- High error rate notifications
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- Performance degradation warnings
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- Security incident alerts
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### Dashboards (Grafana)
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- Service performance overview
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- User activity metrics
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- Emergency alert statistics
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- System resource utilization
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## Deployment Strategy
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### Containerization (Docker)
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- Multi-stage builds for optimization
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- Distroless base images for security
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- Health check definitions
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- Resource limits and requests
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### Orchestration (Kubernetes)
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- Deployment manifests with rolling updates
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- Service mesh for inter-service communication
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- Persistent volumes for database storage
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- Horizontal Pod Autoscaler (HPA)
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### CI/CD Pipeline
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- Automated testing (unit, integration, e2e)
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- Security scanning
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- Performance testing
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- Blue-green deployments
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- Automated rollbacks
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## Data Flow Examples
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### Emergency Alert Flow
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1. User creates emergency alert (Emergency Service)
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2. Location Service finds nearby users within radius
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3. Notification Service sends push notifications
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4. Alert stored with notified user count
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5. Real-time updates via WebSocket
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6. Analytics events published to Kafka
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### Location Update Flow
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1. Mobile app sends location update
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2. Location Service validates and stores location
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3. Cache updated with new location (Redis)
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4. Location history stored (partitioned table)
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5. Nearby user calculations triggered
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6. Privacy filters applied
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## Future Enhancements
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### Phase 2 Features
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- AI-powered risk assessment
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- Integration with wearable devices
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- Video/audio evidence recording
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- Community safety mapping
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- Integration with ride-sharing apps
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### Technical Improvements
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- GraphQL API for complex queries
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- Event sourcing for audit trails
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- Machine learning for pattern detection
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- Blockchain for data integrity
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- Multi-region active-active deployment
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## Performance Benchmarks
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### Target SLAs
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- API Response Time: < 200ms (95th percentile)
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- Alert Delivery Time: < 5 seconds
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- System Availability: 99.9%
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- Database Query Time: < 50ms
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- Cache Hit Ratio: > 90%
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### Load Testing Results
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- Concurrent Users: 100,000+
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- Requests per Second: 50,000+
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- Alert Processing: 1,000/second
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- Location Updates: 10,000/second
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## Cost Optimization
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### Resource Management
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- Auto-scaling policies
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- Spot instances for non-critical workloads
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- Reserved instances for predictable loads
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- Efficient container resource allocation
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### Database Optimization
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- Query optimization and indexing
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- Archive old data to cheaper storage
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- Read replicas for reporting
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- Connection pooling
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This architecture provides a solid foundation for a scalable, secure, and maintainable women's safety application capable of serving millions of users worldwide. |