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J2EE application servers provide scalable performance for a wide range of applications. However, the general-purpose nature of J2EE, which aims to address the needs of every enterprise, also limits its ability to provide a best-of-breed solution for mission-critical applications. In particular, data-intensive applications expose a serious data bottleneck in all J2EE server architectures.
A recent survey of 360 J2EE users found that 57 percent of application performance and availability issues can be traced to inefficient data access problems, and only 42 percent of applications perform as planned during initial deployment. Not surprisingly, the survey went on to state that Java applications fail to meet user expectations 60 percent of the time. Worse yet, a 2004 survey conducted by Forrester Research found that more than two-thirds of respondents discovered application performance problems only when a user called the help desk.
Typically, J2EE servers convert every request for persistent data into one or more SQL statements. For applications with complex object models and heavy request volumes, this approach creates inevitable problems, as illustrated in Figure 1.
Figure 1. J2EE application server bottleneck. Click on thumbnail to view full-sized image.
This article defines the three most common causes of application data bottlenecks and offers a proactive approach for eliminating them. It also illustrates the architecture using a real-world J2EE application with a data services layer that has been deployed globally and is now providing high performance 24-7.
The two application characteristics that most frequently contribute to data bottlenecks are:
Applications in jeopardy of experiencing serious problems have one of the following three requirements:
While enterprise applications are complex and may perform poorly for a variety of reasons, a good rule of thumb for predicting data bottlenecks is the 50/50 rule. J2EE applications that have more than 50 data classes and/or more than 50 transactions per second during peak times are much more likely to experience serious data bottlenecks. Figure 2 illustrates how to assess your application using the 50/50 rule for data bottlenecks.
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