Tier-Based Designs in Java

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The many layers through which a client request has to pass before it is resolved are called tiers Each tier is associated with one or more types of logic: presentation, business, or data access Two-tier applications typically consist of combined presentation and data layers for example, a client applet that directly accesses a server-side database A three-tier application takes this one step further: A thinner client contacts a servlet or CGI program, which then contacts a database Presentation and data are more clearly separated, and the server side distinguishes business logic from data access logic An n-tier application consists of three or more levels of request progression, including the database One example is a client who contacts a servlet that contacts a set of application servers, each of which can access the database Many of today's Web application systems are n-tier You might wonder, Are n-tier designs really desirable What's the payoff Intuitively, it seems that client/server connectivity becomes more complicated The communication path now consists of client-to-server1-to-server2-to- -serverN-to-database This seems like a route that requires more hops, which means more latencies and more chances for error or crashes That can't be good, can it YES, if it's done right Increased Modularization and Component Reusability Having multiple tiers means dividing the work and routing the parts of the problem at hand to independent modules But what if we didn't do this What if everything were handled in a single server-side program For example, suppose you need to build a server-side module that enables Web users to order books In writing the module, you need to make sure that the following events occur when a Web user presses Confirm on his order:
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Receipt of the interactive client request
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Credit card check Creation of a work order Updating of the customer's profile Updating of the book inventory Generation of the interactive client response
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You could bundle all of these operations in the Confirm-and-Pay-for-a-Book module But suppose two months later application requirements change and you need to handle DVD orders Guess what You need to create a new module; call it the Confirm-and-Pay-for-a-DVD module And here are its necessary operations:
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Receipt of the interactive client request Credit card check Creation of a work order Updating of the customer's profile Updating of the DVD inventory Generation of the interactive client response
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It looks familiar, doesn't it Too bad you can't reuse the functionality in the book module Maybe you can create a more generic module called Confirm-and-Pay-for-a-Product Now suppose you are asked to support a mechanism for the bulk ordering of products For example, suppose a reseller communicates its orders to you nightly and you need to process those orders In this case, you just can't reuse your generic product module because the request type is fundamentally different Once again, you need to create a new module, which must include the following operations:
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Receipt of batch requests For each request: - Credit card check - Creation of a work order - Updating of the customer's profile - Updating of the product inventory
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You should have the idea by now We have been unable to reuse the independent operations involved in coarse modules or components Just as in programming, poor modularization allows little reuse Instead, you get code bloat, replication of functionality, and potentially inconsistent application behavior What's worse is the scalability angle to all of this By failing to reuse functionality and replicating it among coarser modules, you unnecessarily eat up memory on your server machines Instead, if you break these modules into finer-grained components, you will be able to not only reuse functionality but also more efficiently iron out performance bottlenecks Better Distributed Processing and Task Parallelism With increased modularization come more options Assuming that we have the technology (such as Java RMI or CORBA) to connect the modules, it is possible to deploy each of the smaller modules on different machines For example, we could have all of the credit card checking done
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on one host and all of the database updating on another host This is an attractive solution because it allows us to distribute the processing load to different machines In some cases, modularization leads to significantly better performance because one operation is independent of all other operations Consider the updating of the customer profile operation in our earlier example Updating a profile conceptually returns no useful information It is just a necessary procedure Updating product inventory, which follows profile updating, need not wait for it to be completed Theoretically, it could execute in parallel with the profile updating process By being able to farm out the processing of the independent modules to different machines, we can increase parallelism during execution, and better parallelism translates into better performance Of course, to effectively parallelize these tasks, we have to build communication solutions that are asynchronous, that is, that do not require an immediate response Still, once we learn to do that, we can make a real difference in application performance by increasing the level of task parallelism More Efficient Service Replication Along with the luxury of being able to deploy components across multiple machines, we have the opportunity to tackle scalability and performance challenges with a finer level of control That is, we can target our solutions and potentially address a very specific system problem A common problem in any application system is that one or more subtasks in a larger task are slow and become bottlenecks Let's go back to the book-ordering example Suppose that credit card checking takes 3 seconds per request, and that we need to process 100 book orders at once Assume for now that we have only one copy of our application service running on our server machine, and assume that this service is single-threaded This means that:
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The fastest credit check will take at least 3 seconds The slowest credit check will take at least (100 3) = 300 seconds (5 minutes) The average credit check will take at least (50 3) = 150 seconds (25 minutes)
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This is unacceptable Perhaps we can replicate the service 100 times and service all 100 clients at once Then the fastest, slowest, and average times will all be at least 3 seconds For the sake of example, suppose that each copy of the large Confirm-and-Pay-for-a-Product module requires 5 Mb (the code requires much less, but the code to have it function as an application service might very well require that much) Replicating the larger module 100 times even if we have enough hardware to support it means that we need 500 Mb of memory just to deal with 100 clients that want to use this one piece of functionality simultaneously! In contrast, suppose that the credit-check operation itself requires only 1 Mb of that 5 Mb By splitting the Confirm-and-Pay-for-a-Product module into smaller independently distributed components, and making things like credit checking into distinct components, we can limit our replication to those parts that need it For example, we can replicate the credit-check operation 100 times, requiring only 100 Mb, not 500 Mb, of space Clearly, the space-efficiency advantage to finer-grained business logic processing makes n-tier deployment attractive If there are bottlenecks in the application, we can identify the components responsible and replicate them as necessary (much like increasing the number of lanes on a freeway) If our components are more fine grained, the cost of this replication (in terms of memory) is less and thus our application will be able to scale better
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