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M2 M3
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Figure 10-18. Incompatible workers Your first inclination might be to modify A to fire three separate events, which you could then route to the individual methods of B. The problem here is that you re changing one worker to adapt to the structure of another. If later you change B, it might be necessary to change A as well. You should try to design workers to perform a specific task or set of business rules, without worrying about how the rest of the system is structured. The idea is that workers are the experts in a certain area of the business logic. One approach in solving the Worker interaction problem could use event multicasting. If N1 supports multicasting, you could wire N1 to the three separate B methods: M1, M2, and M3. Although interesting, this approach has its drawbacks. First, you re relying on N1 being a multicast signal. Not all components or signals provide support for multicasting. Moreover, notifications that expect a result (such as the outcome of the service provided by B) generally don t use multicasting, because it could be difficult to reconcile multiple results. Another problem is that, in order to fulfill A s request, B must execute three methods in a very specific order. Not all languages or component models support built-in multicasting that guarantees the order of notification delivery. A solution that works
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CHAPTER 10 FUNCTIONAL ROLES
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with all languages, without relying on multicasting, uses a coordinator as a mediator, as shown in Figure 10-19.
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N1() N1() N1()
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N1()
Figure 10-19. Using a coordinator as a mediator between workers Worker A sends the single notification N1 to the Coordinator, which assumes the responsibility of making three separate requests to Worker B. The presence of the Coordinator not only simplifies Worker A, but also protects Worker A from changes to Worker B. For example, if later the method M1 were broken into two smaller methods M1A and M1B, the Coordinator would need to be changed, but not Worker A. Is changing a coordinator better than changing a worker Possibly, because coordinators are usually simpler than workers, making it easier to identify the code to change and reducing the probability of inadvertently changing business logic, which is mostly contained in workers.
Managing State Across a Group of Workers
Many systems are designed as state machines: How the system responds to an event depends on what state the system is in. When a system has multiple workers, an important issue is this: Who manages the state One solution is to use a coordinator, which contains a state machine. The Coordinator might wire the subordinate workers in a state-dependent manner or call the workers in a state-dependent way. The Coordinator might add or remove subordinate workers from its team, based on the system s state. Using a coordinator to manage state allows workers to be oblivious to the state-driven design of the system, so they can better focus on supporting their business functions.
An Example: Managing the Life-Cycle State of an Application
As a concrete example of state management, consider the life cycle of a typical application. When a user runs the application, a number of states are generally involved. These states can be managed by a coordinator that I ll call LifecycleCoordinator. The life-cycle states are the following: Starting up: The application loads, initializes, and brings up its main user interface. Running: The application performs its main functions. Shutting down: The application closes and exits. Each state has substates. Depending on how complex the substates are, you might decide to implement each major state using a dedicated state coordinator, such as a StartupCoordinator, a RunCoordinator, and a ShutdownCoordinator. The main LifecycleCoordinator would then switch states by instantiating one of the state coordinators.
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