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3.3.2 Combined interfaces hurt performance
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Worse, when combining local and remote interfaces, you have performance implications. Though not explicitly declared in the interface, clients of local and remote interfaces have certain expectations. Local clients depend on consistent performance and immediate responses. Remote clients, on the other hand, are more complicated and must anticipate such issues as latency and network congestion. An invocation taking longer than expected should not drag the system to its knees. Also, if performance drives the decision to support both local and remote interfaces, explicitly implementing the local EJB interfaces is not necessary. Even though the bean supports a remote interface, many containers detect that a client and bean live in the same virtual machine, and they optimize accordingly. (Of course, if you re developing an EJB that s intended to be truly portable, you can t make this assumption.) On a similar note, the local-interface performance optimization creates other problems. Memory issues directly affect the implementation of the enterprise beans. Local invocations share the same memory space. The bean s implementation creates defensive copies for variables. On the other hand, since different copies of the objects exist on the local and remote machines, remote invocations implicitly defend. If a bean implementation supported both local and remote
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Antipattern: Local & Remote Interfaces Simultaneously
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interfaces, the implementation would have to err on the side of caution, always creating defensive copies of shared objects. When accessed through the remote interface, redundant copies would be made. Let s take a look at a few variations of this antipattern.
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3.3.3 Mini-antipattern: Ubiquitous Distribution
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When faced with the decision between local and remote interfaces, leaving the door open by designing every interface to be accessed remotely may seem a logical conclusion. Then, you can run the entire system locally, breaking it out across multiple boxes at arbitrary points as needed. You can rely on the container to recognize interactions within the same machine and optimize accordingly. In doing so, you severely limit the flexibility of your interfaces, overcomplicate the implementation of the client code and, all in all, take on monster responsibilities and headaches to match. Given that any interface may be or may not be accessed remotely, you must always program to the distributed case. Your interfaces must guarantee a degree of consistency after a failure. Is the method idempotent in other words, will the result be the same no matter how many times you invoke the method Is it okay to retry a request after a failure If not, should the client throw up and die Is there any state that needs to be cleaned up In a remote context, a designer must anticipate all these issues in the interface design and developers must code for them in the client. On the other hand, while local interfaces still need to manage exceptions, they need not shoulder any of these complications, so exception management can be simplified. Next, you must face the issue of performance and method granularity. Given the cost of remote invocations, remote interface methods must be combined and their frequency limited as much as possible. This combination leads to a lack of flexibility in the interface and duplication across the method implementations as requirements for slightly different functionalities arise. By forcing an interface to handle distribution, the application designer inevitably sacrifices both robustness and ease of maintenance for the sake of performance.
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3.3.4 Mini-antipattern: Transparent Distribution
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While we re building all components of our application for possible distribution, designing our system with the coarse-grained, service-driven approach, we might as well see how the other half lives. If method granularity weren t an issue in distributed performance, would our purely distributed domain model be viable Unfortunately, the answer is, No.
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