Integration events tend to have more complex payloads with additional attributes, as the needs of potential listeners can differ significantly.
This often leads to a more thorough approach to communication, resulting in overcommunication to ensure that all relevant information is effectively shared.
[7] Context Mapping identifies and defines the boundaries of different domains or subdomains within a larger system.
Below are some patterns, according to Eric Evans:[8] Although domain-driven design is not inherently tied to object-oriented approaches, in practice, it exploits the advantages of such techniques.
Similarly, the naked objects pattern holds that the user interface can simply be a reflection of a good enough domain model.
In turn, aspect-oriented programming makes it easy to factor out technical concerns (such as security, transaction management, logging) from a domain model, letting them focus purely on the business logic.
While CQRS does not require domain-driven design, it makes the distinction between commands and queries explicit with the concept of an aggregate root.
The command handler pulls in infrastructure concerns related to saving the aggregate root's state and creating needed contexts (e.g., transactions).
Event storming can aid in discovering subdomains, bounded contexts, and aggregate boundaries, which are key constructs in DDD.
Modeling aggregate roots to output events can isolate internal state even further than when projecting read-data from entities, as in standard n-tier data-passing architectures.
One significant benefit is that axiomatic theorem provers (e.g. Microsoft Contracts and CHESS[14]) are easier to apply, as the aggregate root comprehensively hides its internal state.
Events are often persisted based on the version of the aggregate root instance, which yields a domain model that synchronizes in distributed systems through optimistic concurrency.
A bounded context, a fundamental concept in Domain-Driven Design (DDD), defines a specific area within which a domain model is consistent and valid, ensuring clarity and separation of concerns.
A one-to-one relationship, where each bounded context is implemented as a single microservice, is typically ideal as it maintains clear boundaries, reduces coupling, and enables independent deployment and scaling.
However, other mappings may also be appropriate: a one-to-many relationship can arise when a bounded context is divided into multiple microservices to address varying scalability or other operational needs, while a many-to-one relationship may consolidate multiple bounded contexts into a single microservice for simplicity or to minimize operational overhead.
The choice of relationship should balance the principles of DDD with the system's business goals, technical constraints, and operational requirements.