Building the Right Environment to Support AI, Machine Learning and Deep Learning
As developers, we all make assumptions when programming. Perhaps the biggest assumption we make is that those libraries and tools that ship with the .NET Framework are the best way to accomplish a given task. For example, most developers assume that using ASP.NET's Membership system is the best way to manage user accounts in a website (rather than rolling your own user account store). Similarly, creating a Web Reference to communicate with a web service generates markup that auto-creates a proxy class, which handles the low-level details of invoking the web service, serializing parameters, and so on.
Recently a client made us question one of our fundamental assumptions about the .NET Framework SDK and Web Services by asking, "Why should we use proxy class created by Microsoft Visual Studio to connect to a web service?" In this particular project we were calling a web service to retrieve data, which was then sorted, formatted slightly and displayed in a web page. The client hypothesized that it would be more efficient to invoke the web service directly via the HttpWebRequest class, retrieve the XML output, populate an XmlDocument object, then use XSLT to output the result to HTML. Surely that would be faster than using Visual Studio's auto-generated proxy class, right?
Prior to this request, we had never considered rolling our own proxy class; we had always taken advantage of the proxy classes Visual Studio auto-generated for us. Could these auto-generated proxy classes be inefficient? Would retrieving and parsing the web service's XML directly be more efficient? The only way to know for sure was to test my client's hypothesis. Read more.