[whstl]

That's only true if the DB is equally as problematic, and you programming language/framework itself aren't compounding on the overhead caused by the "10x slower" architecture.

On slower languages with slower runtimes, something that is 10x slower than normal code will have much more overhead than in the examples demonstrated by Casey. It won't be about "30ms vs 25ms" as some people are saying. In the past I remember seeing differences between 400ms and 20ms between JBuilder and .to_json in a critical endpoint in a Rails app, to give one example. Sure, one is "cleaner", but in the end it's a 20x overhead that has no place this case.

Also, the myth that "processors spend time waiting for IO" that is spread across this thread is BS. In reality, that's only true for single-user programs. If the app is part of a distributed system, the CPU time can be used to serve more users. This allows you to significantly delay more complex scalability efforts, which is also precious developer (or DevOps) time.

Not to mention that applying "Clean Code" in the first place also takes precious time, which could be used for features or anything making money, even optimizing the DB. Instead, this time is used to mess up the code in ways that have zero proven efficacy, and some developers instead think are terrible.