A Compensating Service Transaction pattern is correct because it allows the composition to proceed with a contingency plan if one part (like the slow transaction service) fails or times out. This aligns with the S90.08 principle of building resilient compositions that can handle partial failures, unlike an Atomic Transaction which would fail entirely. It provides a more robust and real-world solution than a simple callback or discovery pattern.

The Data Model Transformation pattern is designed to insert transformation logic between services in a composition. This allows each service to evolve its internal data model independently, as the transformation step handles the conversion from one format to another, preventing breaking changes from propagating through the composition chain.

The Compensating Service Transaction pattern is explicitly designed for this scenario. It defines a forward action (e.g., reserve inventory) and a corresponding compensating action (e.g., release inventory). If the overall business activity fails at a later stage (payment), the runtime can execute the predefined compensation logic to maintain data consistency, which is a core requirement in distributed transactions.

The Intermediate Routing pattern introduces a dedicated routing component that sits between composition steps. This component centralizes the routing logic (rules for selecting service instances based on content, headers, etc.), removing it from the core composition logic. This increases flexibility, as routing rules can be changed without modifying the composition itself.

State Deferral is the critical capability. It involves the composition actively persisting its current state and context to a durable store before pausing. When the external event (human approval) occurs, the runtime can reload the exact state and resume execution. This is fundamental for managing long-running, interruptible business processes.

A Service Agent pattern can be deployed to intercept messages to and from the problematic service. This agent can act as a watchdog or filter, validating the data format before it reaches the composition logic. If the data is malformed, the agent can reject it, trigger a retry, or provide default values, thereby insulating the composition from the downstream service’s instability.

The Service Callback pattern enables an event-driven architecture. Instead of polling, the composition registers a callback endpoint with the legacy system (or an intermediary). When the legacy system has new data, it invokes the callback, notifying the composition. This is more efficient and real-time than periodic polling.

The primary benefit of Process Centralization (or orchestration) is the consolidation of business process flow and state management into a central coordinator. This provides a single, clear point of control, visibility, and modification for the entire workflow. It simplifies debugging, auditing, and adapting the business process without needing to change multiple distributed services.

Runtime Service Discovery is the pattern that addresses this exact need. It involves a discovery mechanism (often a registry) where services register their instances. The composition logic, or its runtime infrastructure, queries this registry to obtain the current, viable endpoint(s) for a required service, enabling dynamic and resilient connections in elastic environments.

A Compensating Service Transaction pattern is correct because it allows the composition to proceed with a contingency plan if one part (like the slow transaction service) fails or times out. This aligns with the S90.08 principle of building resilient compositions that can handle partial failures, unlike an Atomic Transaction which would fail entirely. It provides a more robust and real-world solution than a simple callback or discovery pattern.0