The tests he put together here are hardly something I'd call a straw-man argument, they seem like reasonable simplification of real-cases.
The focus on performance here ignores the fact that most programs are large systems of many things that interact with each other. That is where good design and abstractions and “clean code” can really help.
Like all things it is about finding a balance and applying the right techniques to the right parts of a larger system.
To me the interesting point is the reminder that there is an innate tension between going fast and being "clean" (i.e. maintanable/understandable). And once you are aware of it you can make your decisions in an informed way. Too often this tension is forgotten/ignored/dogmatically put to the back ("performance doesn't matter over cleanliness" and the likes).
Mind you, I'm also of the camp that performance is very secondary to cleanliness in modern enterprises, but I appreciate a reminder of just how much we are sacrificing on this altar.
It is not an excellent argument for why 'clean code' is not a better fit for the other 99% of their codebase.
For example, if the code is to be distributed and extended as a third-party library, then the class hierarchy is probably a better fit to allow extensibility.
But if the purpose of the API is to compute the area of given shapes (as in the example), then it makes sense to make it efficient and there is no use to provide extensibility to the outside world.
The advocates of "clean code" that Casey mentions will go for extensibility no matter the use.
I find it very interesting to have numbers to weight what you are leaving when you go the extensibility route instead of the non-pessimistic route.
Unfortunately this is just some catchy stereotyping that probably doesn't match reality.
The process was "think about the model, design a class hierarchy that fits with the model, add the operations". Even if I don't think about extensibility, that's how I thought my code.
Why? Because when I think about performance, I used to think about algorithms and I/O optimizations (for example batching).
Now I'll look into this data-oriented programming-thing and see how that applies to my platform (embedded Java) :D
That's a fairly rare breed for sure, but the real problem was that nobody called him out. Well I did, but I'm no longer working there. I couldn't.
You should check if your code is in the hot path before optimizing, because the more you couple things together the harder it is to change it around. For instance, in Casey's example, if you wanted to add a polygon shape but you've optimized calculating area into multiplying height x width by a coefficient, that requires a significant refactor. If you are sure you don't need polygons, that's a perfectly fine optimization. But if you do, you need to start deoptimizing.
Paraphrasing Russ Ackoff, doing the right thing and doing a thing right is the difference between wisdom or effectiveness and efficiency. What Casey is doing here may be efficient, but calculating a billion rectangles doesn't present a realistic or general use case.
"Clean Code" or any paradigm of the sort aims to make qualitative, not quantitative improvements to code. What you gain isn't performance but clarity when you build large systems, reduction in errors, reduce complexity, and so on. Nobody denies that you can make a program faster by manually squeezing performance out if it. But that isn't the only thing that matters, even if it's something you can easily benchmark.
Looking at a tiny code example tells you very little about the consequences of programming like this. If we program with disregard for the system in favour of performance of one of its parts, what does that mean three years down the line, in a codebase with millions of lines of code?`That's just one question.