# Ticket Scaling Microservice - Design Document

## Table of Contents
1. [Architecture Overview](#architecture-overview)
2. [System Components](#system-components)
3. [Scalability Strategies](#scalability-strategies)
4. [Atomic Operations](#atomic-operations)
5. [Fallback Mechanisms](#fallback-mechanisms)
6. [Performance Optimizations](#performance-optimizations)
7. [Monitoring & Observability](#monitoring--observability)
8. [Security Considerations](#security-considerations)
9. [Deployment Strategy](#deployment-strategy)
10. [Future Enhancements](#future-enhancements)

## Architecture Overview

### High-Level Architecture
```
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Load Balancer │    │   Prometheus    │    │    Grafana      │
│   (Optional)    │    │   Monitoring    │    │   Dashboard     │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         │                       │                       │
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│                 │    │                 │    │                 │
│  Ticket Service │◄───┤     Redis       │    │  In-Memory      │
│  (Node.js/      │    │   Primary Store │    │  Fallback Store │
│   Express)      │    │                 │    │                 │
│                 │    │                 │    │                 │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │
         │
┌─────────────────┐
│   PDF Generator │
│   (PDFKit)      │
└─────────────────┘
```

### Design Principles
1. **High Availability**: Fallback mechanisms ensure service continuity
2. **Atomic Operations**: Redis Lua scripts prevent race conditions
3. **Horizontal Scalability**: Stateless design enables easy scaling
4. **Observability**: Comprehensive logging and metrics
5. **Performance**: Optimized for high-throughput scenarios

## System Components

### 1. Core Application (server.js)
- **Technology**: Node.js with Express framework
- **Responsibilities**:
  - HTTP request handling
  - Business logic orchestration
  - Error handling and logging
  - PDF generation coordination

### 2. Redis Client (redis-client.js)
- **Technology**: Redis with Lua scripting
- **Responsibilities**:
  - Atomic ticket operations
  - Event metadata management
  - Connection health monitoring
  - Script execution

### 3. Fallback Store (fallback-store.js)
- **Technology**: In-memory JavaScript Map
- **Responsibilities**:
  - Emergency ticket storage
  - Temporary operation continuity
  - Graceful degradation

### 4. PDF Generator (pdf-generator.js)
- **Technology**: PDFKit library
- **Responsibilities**:
  - Professional ticket generation
  - File management
  - Cleanup operations

### 5. Logging System (logger.js)
- **Technology**: Winston logging framework
- **Responsibilities**:
  - Structured logging
  - Request tracking
  - Error reporting
  - Performance metrics

## Scalability Strategies

### Horizontal Scaling
```
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Instance 1    │    │   Instance 2    │    │   Instance N    │
│   Port: 3049    │    │   Port: 3050    │    │   Port: 305X    │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         └───────────────────────┼───────────────────────┘
                                 │
                    ┌─────────────────┐
                    │  Shared Redis   │
                    │    Cluster      │
                    └─────────────────┘
```

**Key Features**:
- Stateless application design
- Shared Redis backend
- Load balancer distribution
- Independent scaling

### Vertical Scaling
- **CPU**: Multi-core utilization through Node.js cluster mode
- **Memory**: Configurable heap sizes for high-throughput
- **I/O**: Async operations prevent blocking

### Database Scaling
```
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Redis Master  │    │  Redis Replica  │    │  Redis Replica  │
│   (Read/Write)  │───▶│   (Read Only)   │    │   (Read Only)   │
└─────────────────┘    └─────────────────┘    └─────────────────┘
```

**Strategies**:
- Redis clustering for horizontal scaling
- Read replicas for metrics/stats queries
- Sharding by event ID for massive scale

## Atomic Operations

### Lua Script Design
Our core purchase operation uses a Redis Lua script to ensure atomicity:

```lua
-- Atomic ticket purchase script
local ticketKey = KEYS[1]  -- event:X:tickets
local metaKey = KEYS[2]    -- event:X:meta
local globalKey = KEYS[3]  -- global:stats

-- Atomic operations:
1. Check event exists
2. Pop ticket from list
3. Update sold count
4. Update global stats
5. Store purchase record
```

**Benefits**:
- **Race Condition Prevention**: All operations execute atomically
- **Consistency**: No partial state updates
- **Performance**: Single round-trip to Redis
- **Reliability**: All-or-nothing execution

### Concurrency Handling
- **Optimistic Locking**: Lua scripts handle concurrent access
- **Queue Management**: Redis lists provide FIFO ticket distribution
- **Connection Pooling**: Efficient Redis connection reuse

## Fallback Mechanisms

### Activation Triggers
1. **Redis Connection Failure**: Network issues or Redis downtime
2. **Script Execution Errors**: Lua script failures
3. **Timeout Scenarios**: Slow Redis responses

### Fallback Architecture
```
┌─────────────────┐
│  Request Comes  │
└─────────────────┘
         │
         ▼
┌─────────────────┐    ┌─────────────────┐
│  Try Redis      │───▶│  Redis Success  │
│  Operation      │    │  Return Result  │
└─────────────────┘    └─────────────────┘
         │
         ▼ (On Failure)
┌─────────────────┐    ┌─────────────────┐
│  Activate       │───▶│  In-Memory      │
│  Fallback Store │    │  Operation      │
└─────────────────┘    └─────────────────┘
```

### Fallback Limitations
- **Non-Persistent**: Data lost on restart
- **Single Instance**: No cross-instance synchronization
- **Capacity Limited**: Memory constraints
- **Warning Logs**: Clear indication of degraded mode

## Performance Optimizations

### Application Level
1. **Async Operations**: Non-blocking I/O throughout
2. **Connection Pooling**: Reuse Redis connections
3. **Batch Operations**: Bulk ticket seeding
4. **Caching**: Event metadata caching

### Redis Optimizations
1. **Lua Scripts**: Reduced network round-trips
2. **Pipeline Operations**: Batch commands
3. **Memory Management**: Efficient data structures
4. **Persistence**: AOF for durability

### PDF Generation
1. **Async Generation**: Non-blocking PDF creation
2. **Stream Processing**: Memory-efficient file handling
3. **Cleanup Jobs**: Automatic old file removal
4. **Error Isolation**: PDF failures don't affect purchases

## Monitoring & Observability

### Metrics Collection
```json
{
  "global": {
    "totalEvents": 5,
    "totalTickets": 50000,
    "totalSold": 1250
  },
  "events": [
    {
      "eventId": "1",
      "soldTickets": 250,
      "remainingTickets": 9750
    }
  ],
  "system": {
    "usingFallback": false,
    "redisConnected": true,
    "uptime": 3600,
    "memoryUsage": {...}
  },
  "pdf": {
    "totalTickets": 1250,
    "totalSizeMB": "15.6"
  }
}
```

### Logging Strategy
- **Structured Logging**: JSON format for parsing
- **Request Tracking**: Unique IDs for tracing
- **Performance Metrics**: Response times and throughput
- **Error Categorization**: Different log levels

### Health Checks
- **Application Health**: `/health` endpoint
- **Redis Connectivity**: Connection status
- **Fallback Status**: Degraded mode indication
- **Resource Usage**: Memory and CPU monitoring

## Security Considerations

### Input Validation
- **Event ID Validation**: Numeric constraints
- **Request Rate Limiting**: DDoS protection
- **Parameter Sanitization**: Injection prevention

### Container Security
- **Non-Root User**: Principle of least privilege
- **Minimal Base Image**: Alpine Linux for smaller attack surface
- **Health Checks**: Container monitoring

### Data Protection
- **No Sensitive Data**: Tickets are identifiers only
- **Audit Logging**: Purchase tracking
- **Secure Defaults**: Production-ready configuration

## Deployment Strategy

### Development Environment
```bash
# Local development
npm install
npm run docker:up    # Start Redis
npm run seed         # Seed events
npm run dev          # Start with nodemon
```

### Production Environment
```bash
# Docker deployment
docker-compose up -d                    # Core services
docker-compose --profile monitoring up # With monitoring
```

### Container Orchestration
- **Docker Compose**: Local and small deployments
- **Kubernetes**: Large-scale deployments
- **Health Checks**: Automatic restart on failure
- **Resource Limits**: CPU and memory constraints

## Future Enhancements

### Performance Improvements
1. **Redis Clustering**: Horizontal database scaling
2. **CDN Integration**: PDF delivery optimization
3. **Caching Layer**: Application-level caching
4. **Connection Optimization**: Advanced pooling

### Feature Additions
1. **QR Code Generation**: Enhanced ticket security
2. **Email Integration**: Automatic ticket delivery
3. **Payment Processing**: Complete purchase flow
4. **Event Management**: Dynamic event creation

### Monitoring Enhancements
1. **Distributed Tracing**: Request flow tracking
2. **Custom Dashboards**: Business metrics visualization
3. **Alerting**: Proactive issue detection
4. **Performance Profiling**: Bottleneck identification

### Security Hardening
1. **Authentication**: API key management
2. **Rate Limiting**: Advanced throttling
3. **Encryption**: Data in transit protection
4. **Audit Trails**: Comprehensive logging

## Conclusion

This design provides a robust, scalable foundation for high-volume ticket sales with the following key strengths:

- **Atomic Operations**: Guaranteed consistency under load
- **High Availability**: Graceful degradation capabilities
- **Observability**: Comprehensive monitoring and logging
- **Scalability**: Horizontal and vertical scaling support
- **Performance**: Optimized for high-throughput scenarios

The architecture successfully handles the challenge requirements of processing thousands of concurrent requests while maintaining data integrity and system reliability.