Refactoring and performance

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This is an excerpt from Martin Fowler 1999, p. 68-70.

A performance tuning example

Takes Awhile to Create Nothing by Ron Jeffries
The Chrysler comprehensive compensation pay process was running too slowly. Although we were still in development, it began to bother us, because it was slowing down the tests.
Kent Beck, Martin Fowler, and I decided we'd fix it up. While I waited for us to get together, I was speculating, on the basis of my extensive knowledge of the system, about what was probably slowing it down. I thought of several possibilities and chatted with folks about the changes that were probably necessary. We came up with some really good ideas about what would make the system go faster.
Then we measured performance using Kent's profiler. None of the possibilities I had thought of had anything to do with the problem. Instead, we found that the system was spending half its time creating instances of date. Even more interesting was that all the instances had the same couple of values.
When we looked at the date-creation logic, we saw some opportunities for optimizing how these dates were created. They were all going through a string conversion even though no external inputs were involved. The code was just using string conversion for convenience of typing. Maybe we could optimize that.
Then we looked at how these dates were being used. It turned out that the huge bulk of them were all creating instances of date range, an object with a from date and a to date. Looking around little more, we realized that most of these date ranges were empty!
As we worked with date range, we used the convention that any date range that ended before it started was empty. It's a good convention and fits in well with how the class works. Soon after we started using this convention, we realized that just creating a date range that starts after it ends wasn't clear code, so we extracted that behavior into a factory method for empty date ranges.
We had made that change to make the code clearer, but we received an unexpected payoff. We created a constant empty date range and adjusted the factory method to return that object instead of creating it every time. That change doubled the speed of the system, enough for the tests to be bearable. It took us about five minutes.
I had speculated with various members of the team (Kent and Martin deny participating in the speculation) on what was likely wrong with code we knew very well. We had even sketched some designs for improvements without first measuring what was going on.
We were completely wrong. Aside from having a really interesting conversation, we were doing no good at all.
The lesson is: Even if you know exactly what is going on in your system, measure performance, don't speculate. You'll learn something, and nine times out of ten, it won't be that you were right!

Fowler's advice

Refactoring and Performance
A common concern with refactoring is the effect it has on the performance of a program. To make the software easier to understand, you often make changes that will cause the program to run more slowly. This is an important issue. I'm not one of the school of thought that ignores performance in favor of design purity or in hopes of faster hardware. Software has been rejected for being too slow, and faster machines merely move the goalposts. Refactoring certainly will make software go more slowly, but it also makes the software more amenable to performance tuning. The secret to fast software, in all but hard real-time contexts, is to write tunable software first and then to tune it for sufficient speed.
I've seen three general approaches to writing fast software. The most serious of these is time budgeting, used often in hard real-time systems. In this situation, as you decompose the design you give each component a budget for resources—time and footprint. That component must not exceed its budget, although a mechanism for exchanging budgeted times is allowed. Such a mechanism focuses hard attention on hard performance times. It is essential for systems such as heart pacemakers, in which late data is always bad data. This technique is overkill for other kinds of systems, such as the corporate information systems with which I usually work.
The second approach is the constant attention approach. With this approach every programmer, all the time, does whatever he or she can to keep performance high. This is a common approach and has intuitive attraction, but it does not work very well. Changes that improve performance usually make the program harder to work with. This slows development. This would be a cost worth paying if the resulting software were quicker, but usually it is not. The performance improvements are spread all around the program, and each improvement is made with a narrow perspective of the program's behavior.
The interesting thing about performance is that if you analyze most programs, you find that they waste most of their time in a small fraction of the code. If you optimize all the code equally, you end up with 90 percent of the optimizations wasted, because you are optimizing code that isn't run much. The time spent making the program fast, the time lost because of lack of clarity, is all wasted time.
The third approach to performance improvement takes advantage of this 90 percent statistic. In this approach you build your program in a well-factored manner without paying attention to performance until you begin a performance optimization stage, usually fairly late in development. During the performance optimization stage, you follow a specific process to tune the program.
You begin by running the program under a profiler that monitors the program and tells you where it is consuming time and space. This way you can find that small part of the program where the performance hot spots lie. Then you focus on those performance hot spots and use the same optimizations you would use if you were using the constant attention approach. But because you are focusing your attention on a hot spot, you are having much more effect for less work. Even so you remain cautious. As in refactoring you make the changes in small steps. After each step you compile, test, and rerun the profiler. If you haven't improved performance, you back out the change. You continue the process of finding and removing hot spots until you get the performance that satisfies your users. McConnell gives more information on this technique.
Having a well-factored program helps with this style of optimization in two ways. First, it gives you time to spend on performance tuning. Because you have well-factored code, you can add function more quickly. This gives you more time to focus on performance. (Profiling ensures you focus that time on the right place.) Second, with a well-factored program you have finer granularity for your performance analysis. Your profiler leads you to smaller parts of the code, which are easier to tune. Because the code is clearer, you have a better understanding of your options and of what kind of tuning will work.
I've found that refactoring helps me write fast software. It slows the software in the short term while I'm refactoring, but it makes the software easier to tune during optimization. I end up well ahead.

See also

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