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You must take special care to ensure that loggers corresponding to a defunct thread do not loiter around. If you use thread-local storage, when the thread terminates, the associated logger no longer has a strong reference to reach it (unless some other part of the system also keeps a reference to it), and it becomes a candidate for garbage collection. In other cases, you could advise the thread termination join point to make the logger a candidate for garbage collection.
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5.6 Extending logging for other usage
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With very little modification, the basic logging mechanism can be used for other purposes, such as testing, profiling, recovering lost data, and logging user actions. AspectJ-based solutions for such requirements offer essentially the same benefits that we discussed for logging: modularization, noninvasiveness, ease of evolution, and ease of understanding. Let s look at a couple of other ways you can extend the basic logging mechanism. 5.6.1 Testing One way to ensure the accuracy of programs is to use regression testing, in which the testing apparatus checks for the preservation of the output of a specified set of input across all iterations of a module s evolution. You provide a set of input that covers a sufficient range of possible options. Each time you test the program, you use this same input, and the output is expected to be the same. With AspectJ, you can log the output as well as the intermediate steps that have been taken while performing the operation. Then you can compare the actual log with the expected log to check for the preservation of the system behavior. Reproducing bugs is often a hard task you see a bug, but you do not know what actions led to it. Often, the least-suspected step is the cause of the bug. During the QA phase, you can enable the system to log the steps that are followed during testing. When a bug appears, you can get a much better idea about the cause of the bug by looking at the log of the operations performed prior to encountering the bug. In the final shipped product, you can simply remove the logging aspect by excluding it from the final build. Of course, you can implement all this functionality using traditional techniques, which require embedding logging into each module.
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5.6.2 Profiling Profiling a system means collecting the information about the time spent by each operation in the system. By extending the core AspectJ-based logging concepts, you can create profiling functionality without needing invasive changes. The conventional profiling solutions often depend on using specific tools, and they sometimes need to execute the applications in a special VM in order to gather the profile data. These requirements make profiling practical only in the implementation phase, and it is seldom used in a deployed situation. With AspectJ-based profiling, you can use profiling in the deployed system as well, without the need for any special tools or VM, just by embedding the profiling logic in separate aspects. A typical profiling process often needs to gradually focus on certain parts of the system to gather more detailed timing information. The modular, plug-and-play approach of AspectJ adds noninvasive modification to the focus of profiling by limiting the modification to only the profiling aspect. For example, you can change the list of classes and methods that will be profiled by modifying the pointcuts in the profile aspect. A simple way to implement profiling is to augment the invocation timestamp of each log message. You will need to provide both before and after advice to the profiled methods and log their invocation time. Although you could also use an around advice, using both before and after advice is often cheaper due to the cost associated with the creation of the around advice and the unwrapping of the return value. You can use the call depth idea presented in section 5.5.2 to ensure the correct matching of the entry and exit log statements. You can write a log analyzer to extract and process the log information to provide the profiling data, such as the duration of the execution of the methods and the number of invocations of the methods. An alternative approach to using timestamped logging messages for the purpose of profiling is to keep the profile data in memory. Under this scheme, you save the profile information inside an object, typically a map, in the profile aspect. Since a thisJoinPointStaticPart object is the same to all the invocations of a join point, you may use it as the key for the profile data map and store the profile information that is associated with the join point. The map s values contain typical profile data, such as the cumulative average, the longest and shortest duration, and the invocation count. In the before and after advice, instead of logging the timing information to an output stream, you can update the profile data held inside the map. Periodically, or in response to some request, the profiling aspect supplies the data to interested parties. As with the other logging-
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