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these tests. Finally, we carry out a performance test to illustrate the methodology and the usage of the tools. The methodology and toolkit are useful in two basic situations: Performance testing a complete application Designing for performance (examining the performance costs of various aspects of the Java EE API and how certain design decisions will impact overall performance) In the first scenario, we treat the application like a black box. We test the application under various user loads and investigate the performance of every request made by the users. The data is analyzed requests that don t meet the required criteria are looked for, and opportunities for improving performance are identified. While the aforementioned is useful, our advice is to performance test as early in the development cycle as possible. This way, you can use the methodology and the data you obtain from it to help you design for performance, rather than performance testing after the fact. The example presented in this chapter focuses on designing for performance, rather than testing a complete application. If you are interested in learning more about the methodology of testing an application and performance testing in general, you can refer to J2EE Performance Testing with BEA WebLogic Server, by Peter Zadrozny (Apress, 2003). In this chapter, we present an adaptation of the methodology presented in the aforementioned book, which is narrowly focused on the example at hand.

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Figure 10-3. Content type creation form There are other optional settings that you should consider carefully when creating a new content type. First are the Publishing options (see Figure 10-4). In the left vertical menu, click on the Publishing options.

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The focus of the testing methodology is consistency of data measurement. The following list gives a high-level overview of the steps that are involved in the methodology. We present them and describe them in the logical order in which they will be carried out when performing each test: 1. Define the performance criteria. We must define the relevant performance metrics for the specific application in question, and set a realistic target for that metric (e.g., a maximum acceptable response time). 2. Accurately simulate the application usage. The key aspect of this is the definition of the test scripts. These are configuration files that contain a set of requests that represent typical usage profiles of the application. 3. Define the test metrics. These include the duration of the tests, the size of the sample, the amount of initial data to exclude, and others. 4. Perform the tests.

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Depending on the type of application, your focus will vary between two basic performance indicators: response time and throughput When working with synchronous interactive applications, we define a maximum acceptable response time This is the maximum amount of time we are willing to wait before we get a response from the application For a batch or back-end application, we define the minimum acceptable throughput, typically as transactions per second (TPS) but this has to be based on a solid understanding of exactly how a transaction will be defined in your system Each of these metrics is inextricably linked to one another; however, we have not been able to find any mathematical or geometrical relation between them Our advice is to define your performance metrics clearly and unambiguously, and to test to well-defined requirements Not doing so is an open invitation to test and tune endlessly.

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Figure 10-4 Publishing options Depending on whether you want content to be automatically published (made viewable on your site immediately upon saving) and whether you want the content to automatically appear on the homepage of your website, you may wish to adjust these options For our Event content type, we want Events to be automatically published when they are saved, but we don t want them to automatically show up on the homepage So we will uncheck the Promoted to front page checkbox.

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