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Profiling Apps 1 of N: The MVC ActionLink

2010-03-09 15:34
nbsp; I rsquo;m starting a new series of blogs posts on profiling, where we rsquo;ll try and cover common bottlenecks and how to identify them in your applications. However, before delving deeper into the subject, let me make a small but important observation: Your bottleneck is probably not your for loop Now, replace that for loop with switch statement, an older version of some outdated algorithm that you feel needs optimizing, or that retched collection of classes that would perform better if you were using an array to loop through them, and you rsquo;ll end up with the same observation. Premature Optimization When dealing with business applications, it is unusual for major performance problems to be pinpointed down to specific portions of code or a concrete implementation of an algorithm. Usually most of the issues are bottlenecks at the data level, network level or purely down to how a business decision is made. Whether we use an ORM or use SQL directly, incorrectly formulated queries are one of the most predominant causes of bad performance. Not understanding concepts such as Lazy or Eager loading when using an ORM can be disastrous to the performance of an application, and are usually portrayed as ldquo;ORM XYZ sucks at performance rdquo;. nbsp; Network bandwidth and latency are other issues; when dealing with web applications for instance, having large pages (i.e. ViewState) or rendering Javascript directly without using script files, are a common problem for performance penalties. Making heavy calls to the server when very little information is required (i.e. UpdatePanel used incorrectly) are again main causes for concern. In many cases, design decisions we make early can affect the performance of our applications, and it is important to identify these concerns and address them correctly. Using an ORM, profiling it, understanding how Ajax really works and not worrying about working with Javascript, or using an asynchronous architecture when dealing with long running business processes are many ways to avoid bad performance in the long run. On the other hand, what we shouldn rsquo;t do is focus on micro-optimizations, on trying to make the most efficient, yet completely undecipherable algorithm to calculate the probability of winning money when buying lottery tickets, when the underlying problem is a bottleneck caused by a bad query. This kind of approach is often referred to as Premature Optimization, and can be disastrous for a project. When it does boil down to code Having said all that, there are times when we need better performance after having eliminated all the obvious causes, and need to discover why something is not performing as well as it should be. Of course, these concerns are greater when the nature of our application demands highly optimized code. In these cases, it is crucial to understand how things work in order to solve the problem. As a old-school boy, before we had managed libraries and drag-n-drop, I rsquo;m also...
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