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need high-end machines to serve as cache servers. Add this to cheaper storage and faster network cards and you get the big picture distributed caching these days is much more affordable than only a few years ago. Figure 18-3 shows the abstraction that a distributed caching layer provides to applications.

The values assigned to the XmlNamespace and XmlTypeNamespace attributes of the SoapType attribute associated with the class might be puzzling at first, until you realize that they are just the URL-encoded strings that define the complete name of the remote object:

against the widespread use of a distributed cache. Only a few years ago, the perception of scalability was different. It was recognized as a problem, but most of the time it could be resolved in the scope of the single Web server.

http://schemas.microsoft.com/clr/nsassem/RemoteComponents/ RemoteComponents, Version=1.0.0.0, Culture=neutral, PublicKeyToken=null

Today, it is different not only do you welcome the possibility of caching on multiple machines, but you also demand an ad hoc layer to do the hard work of data synchronization and retrieval. This is referred to as a distributed caching system.

All methods are marked with a SoapMethod attribute and delegate the execution to _tp, a protected field that they inherit from the RemotingClientProxy base class. Once you understand the rationale behind this code, you can create your proxy class quite easily. The main advantage of translating the wrapped proxy to Visual Basic .NET source code is the ability to get rid of the external metadata DLL and include the class in your project. Regardless of whether it is compiled inside the client application or deployed as sepa rate DLL, you need to recompile a wrapped proxy if the URL of the remote object changes, because the URL is used as an argument of the SoapMethod attribute and therefore must be a constant string.

As mentioned, lack of scalability is the fundamental problem addressed by a distributed cache. Compared to a classic database, a distributed cache is much easier and cheaper to scale and replicate. It is not coincidental that there is a growing interest in NoSQL solutions, which are essentially distributed stores that can be easily and effectively scaled horizontally. Ultimately, a distributed cache and most NoSQL frameworks offer nearly the same set of features.

Many developers don t recommend using SoapSuds for generating metadata assem blies for two reasons. First, the SoapSuds utility has a few problems with methods that take or return complex objects, such as a typed DataSet. Second, many seasoned developers don t like the location transparency feature of the .NET remoting architec ture that is, the ability to make a remote object appear as if it were local and prefer

Commonly based on a cluster of cache servers, distributed cache gains scalability through the addition of new servers and high availability (H/A) through replication of the content on each server. If one cache server goes down, no data is lost because another copy on another server is immediately available to the application. Although high availability remains a natural attribute of a distributed caching system, the real effectiveness of replication has to be measured against the real behavior of the application. Replication is great for applications that do a lot of reads. As you add more servers, you add more read capacity to your cache cluster and improve the responsiveness and availability of the application. At the same time, a heavily replicated cache is not desirable for write-intensive applications. If the application updates the cache frequently, maintaining multiple synchronized copies of the data becomes ineffective.