Event-Carried State Transfer

The Event-Carried State Transfer pattern focuses on transferring state between components using events. Instead of directly exposing and modifying an object’s state, a component publishes an event that contains a complete snapshot of the state to be transferred as a Value Object (or Transfer Object). Another component then consumes the event and uses the provided state information to update its own internal representation. This approach promotes loose coupling, as the source component doesn’t need to know about the target component or its internal workings, only the event schema.

This pattern is particularly useful in distributed systems, microservices architectures, and CQRS (Command Query Responsibility Segregation) implementations. It helps to avoid tight coupling when maintaining data consistency across different services and facilitates asynchronous communication. Careful consideration needs to be given to event versioning and handling partial state updates, but the benefits in terms of decoupling and scalability often outweigh these concerns.

Usage

• Microservices Communication: When multiple microservices need to act upon the same data, this pattern prevents direct database access and ensures each service operates on its own consistent copy of the state.
• CQRS Event Stores: In CQRS architectures, the command side can publish events that carry the state changes to the query side, enabling efficient updates of read models.
• Event Sourcing: This pattern forms a core element of event sourcing, where the history of state-changing events is the application state.
• Domain Events: Implementing domain events for loosely coupling domain logic within a single application or across microservices.

Examples

1. Kafka with Avro: Kafka is frequently used as an event streaming platform. Avro, a data serialization system, is commonly used to define the schema of the state contained within the events. Microservices subscribe to specific Kafka topics and receive Avro-serialized events representing state changes, allowing them to update their local data stores without direct dependencies.

2. RabbitMQ with JSON: The RabbitMQ message broker widely leverages events with associated payloads. For example, an e-commerce system might publish an OrderCreated event containing the complete order details (customer information, products, shipping address) in JSON format. Separate services – a shipping service, a payment service, and a notification service – can subscribe to this event and initiate their respective processes without needing to communicate with the order management service directly after the event occurs.

3. .NET Mediator Pattern (with events): Several .NET libraries, built around the Mediator pattern, use events to propagate state changes. A component raises an event with a payload including the new state. Handlers subscribed to that event can then react accordingly – for instance, updating a cache or triggering an action in another service.