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of patterns that work well together and other sets that don’t. If we understand these patterns and have names for them, then it is much easier to apply them consistently throughout an application and achieve reliable performance. If we weren’t using a fixed set of patterns but coding each subsystem’s DOM lifecycle in an ad hoc fashion, each new piece of code would be a gamble that might introduce a large memory leak or might not. This benchmarking exercise has provided an overview of the issues involved in developing a DHTML rich client that plays well with your web browser for extended periods of time, and it identified places where errors may occur, both in general and in some of the patterns discussed elsewhere in this book. To really stay on top of memory issues, you must give them a place in your development methodology. Always ask yourself what the effect on memory usage will be as you introduce changes to your code, and always test for memory usage during implementation of the change.

Adopting a pattern-based approach to your codebase will help here, as similar memory issues will crop up repeatedly with the same patterns. We know, for example, that backing objects create cyclic references between DOM and nonDOM nodes, and that Remove By Detachment patterns interfere with Create If Not Exists patterns. If we use patterns consciously in our designs, we are less likely to run into these sorts of problems.

It can help to write and maintain automated test scripts and benchmark your changes against them. Writing the test scripts is probably the hardest part of this, as it involves knowledge of how users use your application. It may be that your app will have several types of user, in which case you would do well to develop several test scripts rather than a single average that fails to represent anyone. As with any kind of tests, they shouldn’t be seen as set in stone once written but should be actively maintained as your project evolves.

8.5 Summary

Performance of any computer program is a combination of execution speed and resource footprint. With Ajax applications, we’re working within a highly managed environment, far removed from the operating system and the hardware, but we still have the opportunity to affect performance greatly, based on the way we code.

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CHAPTER 8

Performance

We introduced the practice of profiling, both by using JavaScript libraries and using a native profiler tool such as the Venkman debugger. Profiling helps us to understand where the bottlenecks in our system are, and it also can be used to provide a baseline against which we can measure change. By comparing profiler results before and after a code change, we can assess its impact on the overall execution speed of our application. We also looked at the issue of memory management and showed how to avoid introducing memory leaks into our code, either through generic bad practices or by running afoul of specific issues with the DOM or Internet Explorer. We saw how to measure memory consumption using the tools available to Windows and UNIX

operating systems.

Finally, our benchmark example showed the real impact that attention to these details can have on our code. The role of design patterns was crucial in identifying where the great divergence in memory footprint lay and how to manage it. Performance is an elusive goal—there is always room for a little more optimization—and we have to adopt a pragmatic approach to getting “good enough”

performance from our Ajax apps. This chapter should have provided you with the tools needed to do just that.

8.6 Resources

We looked at a few useful development tools in this chapter.

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Drip, the Internet Explorer leak detector was created by Joel Webber. His blog, http://jgwebber.blogspot.com/2005/05/drip-ie-leak-detector.html, is no longer available, but Drip can currently be found at www.outofhanwell.com/ieleak/.

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Venkman Profiler: www.svendtofte.com/code/learning_venkman/advanced. php#profiling

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Process Explorer: www.sysinternals.com

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