Event Driven Architecture (EDA) is a software design pattern where components communicate through the production and consumption of events. In EDA, when something significant happens in the system (an event), it triggers actions in other parts of the system.

Key differences from request-response:

• Coupling: EDA promotes loose coupling between components, while request-response creates tight coupling
• Communication: EDA uses asynchronous communication, request-response is typically synchronous
• Scalability: EDA systems can scale more easily as components don't wait for responses
• Resilience: EDA systems are more fault-tolerant as components can continue operating independently

Example: In an e-commerce system, when an order is placed (event), it can trigger inventory updates, payment processing, and shipping notifications without the order service needing to know about these downstream systems.

An event is a notification that something significant has happened in the system. Events represent facts about what occurred and are typically immutable.

Key characteristics of events:

• Immutable: Once created, events cannot be changed
• Timestamped: Events have a timestamp indicating when they occurred
• Contextual: Events contain relevant data about what happened
• Past tense: Events describe something that has already happened

Example event structure:

{
  "eventId": "123e4567-e89b-12d3-a456-426614174000",
  "eventType": "OrderPlaced",
  "timestamp": "2024-01-15T10:30:00Z",
  "data": {
    "orderId": "ORD-001",
    "customerId": "CUST-456",
    "amount": 99.99
  }
}

Primary benefits:

1. Loose Coupling: Components don't need to know about each other directly
2. Scalability: Individual components can scale independently
3. Resilience: Failure in one component doesn't bring down the entire system
4. Flexibility: Easy to add new features by subscribing to existing events
5. Real-time Processing: Events can be processed as they occur
6. Audit Trail: Events provide a natural log of what happened in the system
7. Eventual Consistency: Allows for better performance in distributed systems

Business benefits:

• Faster feature development
• Better system reliability
• Improved user experience through real-time updates
• Easier maintenance and debugging

CRUD (Create, Read, Update, Delete):

• Single model for all operations
• Direct database manipulation
• Immediate consistency
• Simple but can become complex as system grows

CQRS:

• Separate models for commands and queries
• Commands change state through business logic
• Queries read from optimized projections
• Eventual consistency between command and query sides

Comparison:

When to use CQRS:

• Complex business logic
• Different read/write performance requirements
• Multiple views of same data needed
• High-scale applications

These are three fundamental message types in distributed systems:

Events:

• Describe something that has already happened
• Named in past tense (OrderPlaced, PaymentProcessed)
• Multiple consumers can react to the same event
• Fire-and-forget nature

Commands:

• Represent an intention to do something
• Named in imperative form (PlaceOrder, ProcessPayment)
• Typically have one handler
• Can be rejected or fail

Queries:

• Request for information
• Don't change system state
• Return data to the caller
• Can be cached and optimized

Example:

Command: PlaceOrder -> Handler processes -> Event: OrderPlaced
Query: GetOrderStatus -> Returns current order state