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Event Driven Design Pattern

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Here are few useful patterns identified as part of this exercise.

  • Event Notification Event Notification is a pattern used when source systems want to send event messages to notify other systems of a change in its domain and doesn’t really care much about the response. Complex flows that run over various event notifications are difficult to visualize and hard to debug and hence required monitoring live systems. Subscriber needs to call the publisher for the details of the state change which reduces the availability and increases the load and traffic.
  • Event-Carried State Transfer In this pattern, the event carries data in such a way that the subscriber need not contact the source system to do further work. An obvious down-side of this pattern is that there’s lots of data schlepped around and lots of copies. But that’s less of a problem in an age of abundant storage. The gain is greater resilience, since the recipient systems can function if the source system becomes unavailable. Latency reduces as there is no remote call required to access information. But it does involve more complexity on the receiver, since it must sort out maintaining all the state, when it’s usually easier just to call the sender for more information when needed.
  • Event-Sourcing Event sourcing uses event-centric approach to persistence. A business object is persisted by storing a sequence of state changing events. Whenever an object’s state changes, a new event is appended to the sequence of events. Since that is one operation it is inherently atomic. An entity’s current state is reconstructed by replaying its events. Events are persisted in an event store. Not only does the event store act as a database of events, it also behaves like a message broker. It provides an API that enables services to subscribe to events.

Event-sourcing has many interesting benefits. The event log provides a strong audit capability. one can recreate historic states by replaying the event log up to a point. We can explore alternative histories by injecting hypothetical events when replaying. Event sourcing make it plausible to have non-durable working copies, such as a Memory Image. Event sourcing becomes problematic when replaying events results depend on interactions with outside systems. One must deal with changes in the schema of events over time.

  • CQRS Command Query Responsibility Segregation (CQRS) uses separate data structures for reading and writing information. CQRS can be combined with the earlier patterns here. In complex domains, a single model to handle both reads and writes gets too complicated, and can be simplified by separating the models. This is particularly appealing when there is difference in access patterns, such as lots of reads and very few writes. But the gain for using CQRS must be balanced against the additional complexity of having separate models.
  • Event-driven architecture pattern the pattern is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events. The event-driven architecture pattern consists of two main topologies, the mediator and the broker. The mediator topology is commonly used when there is a need to orchestrate multiple steps within an event through a central mediator, whereas the broker topology is used when events can be chained without additional orchestration.

When implementing this pattern, one must address various distributed architecture issues, such as remote process availability, lack of responsiveness, and broker reconnection logic in the event of a broker or mediator failure.

It is important to consider the lack of atomic transactions for a single business process. Because event processor components are highly decoupled and distributed, it is very difficult to maintain a transactional unit of work across them. For this reason, when designing application using this pattern, one must continuously think about which events can and can’t run independently and plan the granularity of event processors accordingly. if there are separate processors for something that should be an undivided transaction—this is probably not the right pattern for your application.

It is important when using this pattern to choose a standard data format (e.g., XML, JSON, Java Object, etc.) and establish an event-processor component contract versioning policy at the outset.

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Event Driven Design Pattern. (2019, February 11). GradesFixer. Retrieved December 6, 2021, from
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