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If these scenarios sound all too familiar, you re not alone. Both novice and veterans alike have suffered similar fates at the hands of EJB applications. The sheer number of opportunities for improving EJB performance makes falling into a vicious tuning cycle deceivingly easy. At one end of the spectrum, you can change deployment descriptors quickly to dynamically influence performance. At the opposite end, you can apply high-level design patterns, using general knowledge gained from distributed computing. The difficulty lies in choosing an approach and measuring its impact in isolation. A solution applied in hopes of improving performance for one aspect of our application may cause unwanted secondary effects in other areas. Consequently, performance tuning quickly turns into a delicate balancing act. We become painfully aware that multiple controls often exist for performance with complex interactions. Each new interaction increases the odds that making a change one that theoretically should improve performance may not make a difference. Besides being incredibly frustrating, when thrash-tuning runs rampant, it has the potential to rob us of enormous amounts of development time. The more you scratch it, the more it itches. A sure-fire way to stop this irritation is to use a sound methodology with information derived from automated performance testing.
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9.5.1 Solution: Use a performance testing methodology
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The only defense we ve found against falling prey to Thrash-tuning is the use of tests to gather evidence first, before making an attempt to improve performance. We ve tried predicting whether an optimization would improve performance, and yet, after hours of navel gazing, we remained undecided. Although not as therapeutic as navel gazing, writing tests that measure the impact of changes has given us much better success. To ensure that you re always ratcheting forward toward optimal performance, current performance must be automatically compared to a baseline. Doing so keeps the performance of your application from going off the rails. If you use a methodology like the following, your performance stays on track, never more than one change away from the last baseline:
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Begin with clean and modular code that s easy to understand and modify, and driven by tests that express its intentions and expectations. Choose quantifiable performance goals for the code. Profile the code to identify hot spots with the highest return on investment. Write and run an automated test that baselines current performance.
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Mini-antipattern: Manual Performance Testing
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Make a change intended to improve performance. Run the automated test again to measure the gain (or loss) in performance. Repeat as often as necessary until the application meets its performance goals.
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Notice that we use a test to measure both before and after a tuning change is made. The test tells us if the tuning did any good. If not, we can back out the change to arrive safely at the last good baseline. Bear in mind that we might need to give caches, pools, and other performance-enhancing mechanisms a chance to warm up before taking a measurement. Otherwise, the observed performance may be thrown off by a cold start. In other words, the test environment has to be predictable, and the tests must be repeatable. This easy-to-use formula really shines when applied incrementally to solve the most pressing performance problem at any given time. Once performance has improved in one area, and a test is in place to keep that problem continually in check, you can repeat the formula with the next most important performance problem. We ll use this methodology to confidently tune our catalog service throughout the remainder of this chapter. In fact, we ve already taken the first steps toward our goal. We wrote clean and modular code to make our test pass. Demonstrating our results to our customer prompted them to draft a realistic performance requirement we can measure. Before beginning to hack and slash, we used a profiler to find high-yield tuning opportunities. Now we re ready to begin tuning so that we can deliver on the goal our customer has given us. First, let s make sure we get started on the right foot by avoiding the tedium of manual testing.
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