Create docs/architecture/system-design.md

docs/architecture/system-design.md

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+# System Architecture
+
+## Overview
+
+BackgroundFX Pro is built on a modern microservices architecture designed for scalability, reliability, and performance. The system processes millions of images and videos daily while maintaining sub-second response times.
+
+## Architecture Diagram
+
+```mermaid
+graph TB
+    subgraph "Client Layer"
+        WEB[Web App]
+        MOB[Mobile App]
+        API_CLIENT[API Clients]
+    end
+    
+    subgraph "Gateway Layer"
+        LB[Load Balancer]
+        WAF[WAF/DDoS Protection]
+        CDN[CDN]
+    end
+    
+    subgraph "API Layer"
+        GATEWAY[API Gateway]
+        AUTH[Auth Service]
+        RATE[Rate Limiter]
+    end
+    
+    subgraph "Application Layer"
+        API_SVC[API Service]
+        PROC_SVC[Processing Service]
+        BG_SVC[Background Service]
+        USER_SVC[User Service]
+        BILL_SVC[Billing Service]
+    end
+    
+    subgraph "Processing Layer"
+        QUEUE[Job Queue]
+        WORKERS[Worker Pool]
+        GPU[GPU Cluster]
+        ML[ML Models]
+    end
+    
+    subgraph "Data Layer"
+        PG[(PostgreSQL)]
+        MONGO[(MongoDB)]
+        REDIS[(Redis)]
+        S3[Object Storage]
+    end
+    
+    subgraph "Infrastructure"
+        K8S[Kubernetes]
+        MONITOR[Monitoring]
+        LOG[Logging]
+    end
+    
+    WEB --> LB
+    MOB --> LB
+    API_CLIENT --> LB
+    
+    LB --> WAF
+    WAF --> CDN
+    CDN --> GATEWAY
+    
+    GATEWAY --> AUTH
+    GATEWAY --> RATE
+    GATEWAY --> API_SVC
+    
+    API_SVC --> PROC_SVC
+    API_SVC --> BG_SVC
+    API_SVC --> USER_SVC
+    API_SVC --> BILL_SVC
+    
+    PROC_SVC --> QUEUE
+    QUEUE --> WORKERS
+    WORKERS --> GPU
+    GPU --> ML
+    
+    API_SVC --> PG
+    PROC_SVC --> MONGO
+    AUTH --> REDIS
+    WORKERS --> S3
+    
+    K8S --> MONITOR
+    K8S --> LOG
+```
+
+## Core Components
+
+### 1. Gateway Layer
+
+#### Load Balancer
+- **Technology**: AWS ALB / nginx
+- **Features**:
+  - SSL termination
+  - Health checks
+  - Auto-scaling triggers
+  - Geographic routing
+
+#### WAF & DDoS Protection
+- **Technology**: Cloudflare / AWS WAF
+- **Protection**:
+  - Rate limiting
+  - IP blocking
+  - OWASP rules
+  - Bot detection
+
+#### CDN
+- **Technology**: CloudFront / Cloudflare
+- **Caching**:
+  - Static assets
+  - Processed images
+  - API responses
+  - Edge computing
+
+### 2. API Layer
+
+#### API Gateway
+- **Technology**: Kong / AWS API Gateway
+- **Responsibilities**:
+  - Request routing
+  - Authentication
+  - Rate limiting
+  - Request/response transformation
+  - API versioning
+
+#### Authentication Service
+- **Technology**: Auth0 / Custom JWT
+- **Features**:
+  - JWT token management
+  - OAuth 2.0 support
+  - SSO integration
+  - MFA support
+
+### 3. Application Services
+
+#### API Service
+```python
+# FastAPI service structure
+app/
+├── routers/
+│   ├── auth.py
+│   ├── processing.py
+│   ├── projects.py
+│   └── webhooks.py
+├── services/
+│   ├── image_service.py
+│   ├── video_service.py
+│   └── background_service.py
+├── models/
+│   └── database.py
+└── main.py
+```
+
+#### Processing Service
+- **Queue Management**: Celery + RabbitMQ
+- **Worker Pool**: Auto-scaling based on queue depth
+- **GPU Allocation**: Dynamic GPU assignment
+- **Model Loading**: Lazy loading with caching
+
+### 4. ML Pipeline
+
+#### Model Architecture
+```python
+models/
+├── segmentation/
+│   ├── rembg/           # General purpose
+│   ├── u2net/           # High quality
+│   ├── deeplab/         # Semantic segmentation
+│   └── custom/          # Custom trained models
+├── enhancement/
+│   ├── edge_refine/     # Edge refinement
+│   ├── matting/         # Alpha matting
+│   └── super_res/       # Super resolution
+└── generation/
+    ├── stable_diffusion/ # Background generation
+    └── style_transfer/   # Style application
+```
+
+#### Processing Pipeline
+```python
+def process_image(image: Image, options: ProcessOptions):
+    # 1. Pre-processing
+    image = preprocess(image)
+    
+    # 2. Segmentation
+    mask = segment(image, model=options.model)
+    
+    # 3. Refinement
+    if options.refine_edges:
+        mask = refine_edges(mask, image)
+    
+    # 4. Matting
+    if options.preserve_details:
+        mask = alpha_matting(mask, image)
+    
+    # 5. Composition
+    result = composite(image, mask, options.background)
+    
+    # 6. Post-processing
+    result = postprocess(result, options)
+    
+    return result
+```
+
+### 5. Data Architecture
+
+#### PostgreSQL Schema
+```sql
+-- Core tables
+CREATE TABLE users (
+    id UUID PRIMARY KEY,
+    email VARCHAR(255) UNIQUE,
+    plan_id INTEGER,
+    created_at TIMESTAMP
+);
+
+CREATE TABLE projects (
+    id UUID PRIMARY KEY,
+    user_id UUID REFERENCES users(id),
+    name VARCHAR(255),
+    type VARCHAR(50),
+    created_at TIMESTAMP
+);
+
+CREATE TABLE processing_jobs (
+    id UUID PRIMARY KEY,
+    project_id UUID REFERENCES projects(id),
+    status VARCHAR(50),
+    progress INTEGER,
+    created_at TIMESTAMP,
+    completed_at TIMESTAMP
+);
+```
+
+#### MongoDB Collections
+```javascript
+// Image metadata
+{
+  _id: ObjectId,
+  user_id: String,
+  original_url: String,
+  processed_url: String,
+  mask_url: String,
+  metadata: {
+    width: Number,
+    height: Number,
+    format: String,
+    size: Number,
+    processing_time: Number
+  },
+  processing_options: Object,
+  created_at: Date
+}
+```
+
+#### Redis Usage
+- **Session Management**: User sessions
+- **Caching**: API responses, model outputs
+- **Rate Limiting**: Request counting
+- **Pub/Sub**: Real-time notifications
+- **Job Queue**: Celery broker
+
+## Scalability Design
+
+### Horizontal Scaling
+```yaml
+# Kubernetes HPA configuration
+apiVersion: autoscaling/v2
+kind: HorizontalPodAutoscaler
+metadata:
+  name: api-service-hpa
+spec:
+  scaleTargetRef:
+    apiVersion: apps/v1
+    kind: Deployment
+    name: api-service
+  minReplicas: 3
+  maxReplicas: 100
+  metrics:
+  - type: Resource
+    resource:
+      name: cpu
+      target:
+        type: Utilization
+        averageUtilization: 70
+  - type: Resource
+    resource:
+      name: memory
+      target:
+        type: Utilization
+        averageUtilization: 80
+```
+
+### Database Scaling
+- **Read Replicas**: Geographic distribution
+- **Sharding**: User-based sharding
+- **Connection Pooling**: PgBouncer
+- **Query Optimization**: Indexed queries
+
+### Caching Strategy
+```python
+# Multi-level caching
+@cache.memoize(timeout=3600)
+def get_processed_image(image_id: str):
+    # L1: Application memory
+    if image_id in local_cache:
+        return local_cache[image_id]
+    
+    # L2: Redis
+    cached = redis_client.get(f"img:{image_id}")
+    if cached:
+        return cached
+    
+    # L3: CDN
+    cdn_url = f"https://cdn.backgroundfx.pro/{image_id}"
+    if check_cdn(cdn_url):
+        return cdn_url
+    
+    # L4: Object storage
+    return s3_client.get_object(image_id)
+```
+
+## Performance Optimization
+
+### Image Processing
+- **Batch Processing**: Process multiple images in parallel
+- **GPU Optimization**: CUDA kernels for critical paths
+- **Model Optimization**: TensorRT, ONNX conversion
+- **Memory Management**: Stream processing for large files
+
+### API Performance
+- **Response Compression**: Gzip/Brotli
+- **Pagination**: Cursor-based pagination
+- **Field Selection**: GraphQL-like field filtering
+- **Async Processing**: Non-blocking I/O
+
+## Reliability & Fault Tolerance
+
+### High Availability
+- **Multi-Region**: Active-active deployment
+- **Failover**: Automatic failover with health checks
+- **Circuit Breakers**: Prevent cascade failures
+- **Retry Logic**: Exponential backoff
+
+### Disaster Recovery
+- **Backup Strategy**:
+  - Database: Daily snapshots, point-in-time recovery
+  - Object Storage: Cross-region replication
+  - Configuration: Version controlled in Git
+
+### Monitoring & Observability
+```yaml
+# Monitoring stack
+monitoring:
+  metrics:
+    - Prometheus
+    - Grafana
+  logging:
+    - ELK Stack
+    - Fluentd
+  tracing:
+    - Jaeger
+    - OpenTelemetry
+  alerting:
+    - PagerDuty
+    - Slack
+```
+
+## Security Architecture
+
+### Defense in Depth
+1. **Network Security**:
+   - VPC isolation
+   - Security groups
+   - Network ACLs
+
+2. **Application Security**:
+   - Input validation
+   - SQL injection prevention
+   - XSS protection
+
+3. **Data Security**:
+   - Encryption at rest
+   - Encryption in transit
+   - Key management (AWS KMS)
+
+4. **Access Control**:
+   - RBAC
+   - API key management
+   - OAuth 2.0
+
+## Cost Optimization
+
+### Resource Optimization
+- **Spot Instances**: For batch processing
+- **Reserved Instances**: For baseline capacity
+- **Auto-scaling**: Scale down during low usage
+- **Storage Tiering**: S3 lifecycle policies
+
+### Performance vs Cost
+```python
+# Dynamic quality selection based on plan
+def select_processing_quality(user_plan: str, requested_quality: str):
+    quality_costs = {
+        'low': 1,
+        'medium': 2,
+        'high': 5,
+        'ultra': 10
+    }
+    
+    if user_plan == 'enterprise':
+        return requested_quality
+    elif user_plan == 'pro':
+        return min(requested_quality, 'high')
+    else:  # free
+        return 'low'
+```
+
+## Future Architecture Plans
+
+### Planned Improvements
+1. **Edge Computing**: Process at CDN edge locations
+2. **WebAssembly**: Client-side processing
+3. **Serverless**: Lambda for burst capacity
+4. **AI Optimization**: AutoML for model improvement
+5. **Blockchain**: Decentralized storage option
+
+---
+
+For detailed implementation guides, see:
+- [Deployment Guide](../deployment/README.md)
+- [Scaling Guide](scaling.md)
+- [Security Guide](security.md)