Architecture Ticket Management System

🏗️ Overview

The Ticket Management System is built as a microservices architecture with 7 core services, each handling specific business domains. The system is designed for high scalability, fault tolerance, and maintainability.

📊 Architecture Diagram

┌─────────────────────────────────────────────────────────────────────────────┐
│                              CLIENT APPLICATIONS                            │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐        │
│  │   Web App   │  │ Mobile App  │  │ Admin Panel │  │ Third Party │        │
│  └─────────────┘  └─────────────┘  └─────────────┘  └─────────────┘        │
└─────────────────────────────────────────────────────────────────────────────┘
                                    │
                                    ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                              API GATEWAY (8080)                            │
│  ┌─────────────────────────────────────────────────────────────────────────┐ │
│  │ • Authentication & Authorization                                        │ │
│  │ • Rate Limiting (Redis-based)                                          │ │
│  │ • Request Routing                                                      │ │
│  │ • Load Balancing                                                       │ │
│  │ • Circuit Breaker                                                      │ │
│  │ • Request/Response Logging                                             │ │
│  └─────────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────┘
                                    │
                    ┌───────────────┼───────────────┐
                    │               │               │
                    ▼               ▼               ▼
        ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
        │   USER SERVICE  │ │  EVENT SERVICE  │ │ TICKET SERVICE  │
        │     (8081)      │ │     (8082)      │ │     (8083)      │
        └─────────────────┘ └─────────────────┘ └─────────────────┘
                    │               │               │
                    └───────────────┼───────────────┘
                                    │
                                    ▼
        ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
        │  ORDER SERVICE  │ │ PAYMENT SERVICE │ │NOTIFICATION SVC │
        │     (8084)      │ │     (8085)      │ │     (8086)      │
        └─────────────────┘ └─────────────────┘ └─────────────────┘
                    │               │               │
                    └───────────────┼───────────────┘
                                    │
                                    ▼
                    ┌─────────────────────────────────┐
                    │         INFRASTRUCTURE          │
                    │  ┌─────────┐ ┌─────────┐       │
                    │  │POSTGRES │ │  REDIS  │       │
                    │  │ (5432)  │ │ (6379)  │       │
                    │  └─────────┘ └─────────┘       │
                    │  ┌─────────┐ ┌─────────┐       │
                    │  │  KAFKA  │ │ ZOOKEEPER│       │
                    │  │ (9092)  │ │ (2181)  │       │
                    │  └─────────┘ └─────────┘       │
                    └─────────────────────────────────┘

🔧 Service Details

1. API Gateway Service (Port 8080)

Purpose: Single entry point for all client requests

Responsibilities:

• Authentication & Authorization: JWT token validation via User Service
• Rate Limiting: Redis-based rate limiting per client/IP
• Request Routing: Route requests to appropriate microservices
• Load Balancing: Distribute load across service instances
• Circuit Breaker: Prevent cascade failures
• Request/Response Logging: Centralized logging
• CORS Handling: Cross-origin resource sharing
• API Documentation: Swagger/OpenAPI integration

Key Components:

- AuthenticationFilter: JWT validation
- RateLimitFilter: Redis-based rate limiting
- RouteConfigService: Dynamic route configuration
- RedisService: Caching and rate limiting
- JwtService: Token validation delegation

Communication Patterns:

• Synchronous: Validates JWT tokens with User Service
• Asynchronous: Logs requests/events to Kafka
• Caching: Redis for rate limiting and route configs

---

2. User Service (Port 8081)

Purpose: User management and authentication

Responsibilities:

• User Registration: Create new user accounts
• User Authentication: Login/logout functionality
• JWT Token Management: Generate and validate tokens
• User Profile Management: CRUD operations on user profiles
• Role Management: User roles and permissions
• Password Management: Secure password handling

Key Components:

- UserController: REST endpoints for user operations
- UserService: Business logic for user management
- JwtService: JWT token generation and validation
- UserRepository: Data access layer
- PasswordEncoder: Secure password encoding

Database Schema:

users:
- id (PK)
- username (unique)
- email (unique)
- password (encrypted)
- first_name
- last_name
- phone_number
- role (USER, ADMIN)
- created_at
- updated_at

Communication Patterns:

• Synchronous: REST API calls from other services
• Asynchronous: Publishes user events to Kafka
• Caching: Redis for user session data

---

3. Event Service (Port 8082)

Purpose: Event management and administration

Responsibilities:

• Event CRUD: Create, read, update, delete events
• Event Search: Search and filter events
• Event Categories: Manage event types and categories
• Event Status Management: Active, inactive, cancelled events
• Event Capacity Management: Track available seats
• Event Analytics: Basic event statistics

Key Components:

- EventController: REST endpoints for event operations
- EventService: Business logic for event management
- EventRepository: Data access layer
- EventSearchService: Search and filtering logic

Database Schema:

events:
- id (PK)
- title
- description
- location
- date
- capacity
- available_seats
- base_price
- event_type (CONCERT, SPORTS, THEATER, etc.)
- status (ACTIVE, INACTIVE, CANCELLED)
- created_at
- updated_at

Communication Patterns:

• Synchronous: REST API calls from other services
• Asynchronous: Publishes event updates to Kafka
• Caching: Redis for event listings and search results

---

4. Ticket Service (Port 8083)

Purpose: Ticket inventory and availability management

Responsibilities:

• Ticket Inventory: Manage ticket availability
• Seat Management: Handle seat assignments and conflicts
• Ticket Locking: Temporary ticket holds during purchase
• Ticket Status Management: Available, locked, sold, cancelled
• Expired Lock Cleanup: Automatic cleanup of expired locks
• Ticket Analytics: Availability statistics

Key Components:

- TicketController: REST endpoints for ticket operations
- TicketService: Business logic for ticket management
- TicketRepository: Data access layer
- ScheduledTasks: Cleanup expired locks
- RedisService: Distributed locking

Database Schema:

tickets:
- id (PK)
- event_id (FK)
- seat_number
- price
- ticket_type (VIP, STANDARD, etc.)
- status (AVAILABLE, LOCKED, SOLD, CANCELLED)
- locked_until
- locked_by (user_id)
- created_at
- updated_at

Communication Patterns:

• Synchronous: REST API calls from Order Service
• Asynchronous: Publishes ticket status changes to Kafka
• Distributed Locking: Redis for ticket locking
• Scheduled Tasks: Cleanup expired locks

---

5. Order Service (Port 8084)

Purpose: Order processing and orchestration

Responsibilities:

• Order Creation: Create new orders
• Order Orchestration: Coordinate between services
• Order Status Management: Track order lifecycle
• Payment Integration: Coordinate with Payment Service
• Ticket Reservation: Lock tickets during order process
• Order Cancellation: Handle order cancellations and refunds
• Order History: User order history and tracking

Key Components:

- OrderController: REST endpoints for order operations
- OrderService: Business logic for order management
- OrderEventService: Async event publishing
- UserServiceClient: Feign client for User Service
- TicketServiceClient: Feign client for Ticket Service
- PaymentServiceClient: Feign client for Payment Service

Database Schema:

orders:
- id (PK)
- user_id (FK)
- ticket_id (FK)
- event_id (FK)
- status (PENDING, CONFIRMED, CANCELLED, FAILED)
- total_amount
- quantity
- payment_id
- order_date
- created_at
- updated_at

Communication Patterns:

• Synchronous: REST calls to User, Ticket, and Payment services
• Asynchronous: Publishes order events to Kafka
• Circuit Breaker: Resilience4j for external service calls
• Retry Logic: Automatic retry for failed operations

---

6. Payment Service (Port 8085)

Purpose: Payment processing and management

Responsibilities:

• Payment Processing: Handle various payment methods
• Payment Gateway Integration: Multiple payment gateways
• Payment Status Tracking: Track payment lifecycle
• Refund Processing: Handle payment refunds
• Payment Analytics: Payment statistics and reporting
• Fraud Detection: Basic fraud prevention

Key Components:

- PaymentController: REST endpoints for payment operations
- PaymentService: Business logic for payment processing
- PaymentGatewayService: Integration with payment gateways
- PaymentRepository: Data access layer
- FraudDetectionService: Basic fraud prevention

Database Schema:

payments:
- id (PK)
- order_id (FK)
- user_id (FK)
- amount
- currency
- payment_method (CREDIT_CARD, DEBIT_CARD, etc.)
- status (PENDING, COMPLETED, FAILED, REFUNDED)
- gateway_transaction_id
- gateway_response
- payment_date
- created_at
- updated_at

Communication Patterns:

• Synchronous: REST API calls from Order Service
• Asynchronous: Publishes payment events to Kafka
• External APIs: Payment gateway integrations
• Circuit Breaker: Handle payment gateway failures

---

7. Notification Service (Port 8086)

Purpose: Notification delivery and management

Responsibilities:

• Email Notifications: Send email notifications
• SMS Notifications: Send SMS notifications
• Push Notifications: Mobile push notifications
• Notification Templates: Manage notification templates
• Notification History: Track sent notifications
• Retry Logic: Handle failed notifications
• Notification Preferences: User notification settings

Key Components:

- NotificationController: REST endpoints for notification operations
- NotificationService: Business logic for notification management
- EmailService: Email delivery service
- SmsService: SMS delivery service
- KafkaListener: Consume notification events
- NotificationRepository: Data access layer

Database Schema:

notifications:
- id (PK)
- user_id (FK)
- order_id (FK)
- type (EMAIL, SMS, PUSH)
- subject
- content
- status (PENDING, SENT, FAILED)
- retry_count
- sent_at
- created_at
- updated_at

Communication Patterns:

• Asynchronous: Consumes events from Kafka
• External APIs: Email/SMS service integrations
• Retry Logic: Handle delivery failures
• Batch Processing: Process notifications in batches

---

🔄 Service Communication Patterns

1. Synchronous Communication (REST APIs)

Pattern: Request-Response Use Cases: Real-time operations requiring immediate response

Services Using REST:

• API Gateway ↔ User Service: JWT validation
• Order Service ↔ User Service: User validation
• Order Service ↔ Ticket Service: Ticket operations
• Order Service ↔ Payment Service: Payment processing

2. Asynchronous Communication (Kafka)

Pattern: Event-Driven Architecture Use Cases: Non-blocking operations, notifications, analytics

Event Topics:

• order-created: New order created
• order-confirmed: Order payment confirmed
• order-cancelled: Order cancelled
• payment-processed: Payment completed
• ticket-locked: Tickets locked
• ticket-sold: Tickets sold
• user-registered: New user registered

3. Caching Communication (Redis)

Pattern: Cache-Aside Use Cases: Frequently accessed data, session management

Cached Data:

• User Sessions: JWT tokens, user info
• Event Data: Event listings, search results
• Ticket Availability: Available ticket counts
• Rate Limiting: Request counts per client
• Route Configuration: API Gateway routes

4. Database Communication

Pattern: Repository Pattern Use Cases: Data persistence, transactions

Database Patterns:

• Shared Database: All services use same PostgreSQL instance
• Repository Pattern: Abstract data access
• Transaction Management: ACID compliance
• Connection Pooling: HikariCP for performance

---

🔐 Security Architecture

1. Authentication & Authorization

Security Components:

• JWT Tokens: Stateless authentication
• Role-Based Access Control: User roles and permissions
• API Gateway Security: Centralized security enforcement
• Service-to-Service Security: Internal network security

2. Rate Limiting

Rate Limiting Strategy:

• Per Client/IP: Individual client limits
• Per Endpoint: Different limits for different APIs
• Sliding Window: Time-based rate limiting
• Redis Storage: Distributed rate limiting

---

📈 Scalability Patterns

1. Horizontal Scaling

Scaling Strategies:

• Service Replication: Multiple instances per service
• Load Balancing: Distribute load across instances
• Auto-scaling: Scale based on metrics
• Database Scaling: Read replicas, sharding

2. Caching Strategy

Caching Layers:

• Application Cache: In-memory caching
• Distributed Cache: Redis for shared data
• Database Cache: Query result caching
• CDN Cache: Static content caching

3. Circuit Breaker Pattern

Circuit Breaker Implementation:

• Resilience4j: Circuit breaker library
• Failure Threshold: Configurable failure limits
• Timeout: Automatic recovery attempts
• Fallback: Graceful degradation

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🔍 Monitoring & Observability

1. Health Checks

Health Check Endpoints:

• /actuator/health: Basic health status
• /actuator/health/db: Database connectivity
• /actuator/health/redis: Redis connectivity
• /actuator/health/kafka: Kafka connectivity

2. Metrics Collection

Metrics Types:

• Application Metrics: Request rates, response times
• Business Metrics: Orders, payments, tickets
• Infrastructure Metrics: CPU, memory, disk
• Custom Metrics: Business-specific KPIs

3. Distributed Tracing

Tracing Implementation:

• Sleuth: Distributed tracing
• Zipkin: Trace visualization
• Trace IDs: Request correlation
• Span Tracking: Operation timing

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🚀 Deployment Architecture

1. Container Orchestration

Deployment Components:

• Docker Containers: Service packaging
• Kubernetes: Container orchestration
• Service Mesh: Istio for advanced networking
• CI/CD: Automated deployment pipelines

2. Environment Strategy

Environment Types:

• Development: Local Docker Compose
• Staging: Production-like environment
• Production: High-availability setup

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📋 Service Dependencies

1. Infrastructure Dependencies

Service	Database	Redis	Kafka	External APIs
API Gateway	✅	✅	✅	✅
User Service	✅	✅	✅	❌
Event Service	✅	✅	✅	❌
Ticket Service	✅	✅	✅	❌
Order Service	✅	✅	✅	✅
Payment Service	✅	✅	✅	✅
Notification Service	✅	✅	✅	✅

2. Service Dependencies

Service	Depends On	Communication Type
API Gateway	User Service	Synchronous (JWT validation)
Order Service	User Service, Ticket Service, Payment Service	Synchronous (REST)
Notification Service	All Services	Asynchronous (Kafka)
Payment Service	Order Service	Synchronous (REST)

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🎯 Key Design Principles

1. Single Responsibility Principle

Each service has a single, well-defined responsibility and business domain.

2. Loose Coupling

Services communicate through well-defined APIs and events, not direct dependencies.

3. High Cohesion

Related functionality is grouped together within each service.

4. Fault Tolerance

Services are designed to handle failures gracefully with circuit breakers and fallbacks.

5. Scalability

Services can be scaled independently based on demand.

6. Observability

Comprehensive monitoring, logging, and tracing for all services.

7. Security

Security is implemented in multiple layers with proper authentication and authorization.

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This architecture provides a robust, scalable, and maintainable foundation for the Ticket Management System, capable of handling high loads while maintaining system reliability and performance.