Which is faster,
Hold it right there, buddy. Before answering that question I must give you my standard six-part rant about why I probably cannot sensibly answer questions that begin “which is faster“.
Part the first: Why are you even asking me?
If you have two horses and you want to know which of the two is the faster then race your horses. Don’t write short descriptions of the horses, post them on the Internet, and ask random strangers to guess which is faster! Even if by sheer chance you got an accurate answer, how would you have any confidence in its accuracy? You can easily and accurately discover which of two programs is faster by running both yourself and measuring them with a stopwatch.
(Caveat: writing performance benchmarks that provide high-quality, meaningful results can be somewhat tricky in a garbage-collected, JIT-compiled runtime. I see this done wrong a lot. I will return to this topic in a future blog post.)
Moreover: performance is highly sensitive to things like what hardware you are using, what software you have installed, what other processes are doing, and a host of other factors. You’re the only person who knows what environment the code is going to be run in, so you’re the only person who can do realistic performance testing in that environment.
Part the second: Do you really need to answer that question?
The question presupposes that there actually is a performance problem to be solved. If the code as it stands — the working, debugged, tested code — is already fast enough for your customer then knowing which of two ways to write the code is the faster is just trivia. Spend your valuable time worrying about something else, like testing, robustness, security, and so on. Unnecessary code changes are expensive and dangerous; don’t make performance-based changes unless you’ve identified a performance problem.
Part the third: Is that really the bottleneck?
Suppose that you really do have an identified performance problem: asking which of two things is faster is premature if neither thing is the cause of the problem! The most important thing that I’ve learned about performance analysis is that my highly educated and experienced guess about the root cause of a performance problem is dead wrong probably more than a third of the time. Use a profiler or other analysis tool to determine empirically where the bottleneck is before you start investigating alternatives.
Part the fourth: Is the difference relevant?
Suppose that you have actually identified a bottleneck: now the relevant question is not actually “which horse is faster?” Rather, the relevant question is actually “are either of these horses fast enough to meet my customer’s needs?” If neither horse is fast enough for your purposes then knowing which is faster is irrelevant. And if both are fast enough then you can base your decision on important factors other than performance, as discussed in part the second.
(The question also presupposes that the two alternatives proposed are semantic equivalents. If one of those is a horse and the other is a refrigerator, asking which one runs faster is maybe a non-starter.)
Part the fifth: What is this “faster” you speak of?
There are lots of kinds of speed, and optimizing for one kind can deoptimize for another. I’m sure you’ve all encountered situations where normal-case-scenario performance is acceptable but worst-case-scenario performance is terrible. Anyone who has been unable to get a long-distance phone call placed on Mother’s Day knows what I’m talking about; the telephone network was designed to handle slightly-higher-than-average load, not highest-likely load. Unfortunately, implementing code that ensures an upper bound on your worst-possible-scenario behaviour often ends up making the typical-case-scenario unacceptably slower, and vice versa.
Leaving the difference between best, worst and typical aside, there are all kinds of speed metrics. When I was working on Visual Studio Tools For Office we did comparatively little making the customization framework code run faster because our tests showed that it typically ran fast enough to satisfy customers. But we did an enormous amount of work making the framework code load faster, because our research showed that Office power users were highly irritated by noticable-by-humans delays when loading customized documents for the first time. Similarly, in the web services realm there is a big difference between optimizing for time-to-first-byte, time-to-last-byte and throughput. You have to know what kind of speed is really important to the customer.
Part the sixth: Are you looking at the big picture?
Almost all performance analysis questions I see are solely about improving speed. There are lots of non-speed metrics like memory usage, disk usage, network usage, processor usage, and so on, that might be more relevant than raw speed to your customer. Making code faster often involves trading less time for more memory, which might be a bad trade. Don’t over-focus on speed metrics; look at the big picture.
Well, that rant used up this whole episode. Next time on FAIC: Which is faster,