If you understand how the CPU, memory, network, and disk are processing your program and its structures, you can design your programs to optimize for the hardware.

Full guide for ref: https://github.com/tigerbeetle/tigerbeetle/blob/main/docs/TI...

While I don't agree TigerStyle inherently actually helps you with this, design and architectural mistakes don't usually get solved in the way you're describing. You sometimes have to rethink how to structure your entire codebase. Sometimes you can evolve an architectural improvement but sometimes it requires a complete rewrite and there's no way to get outside of that path dependency.

Most undergrad CS students go through an algorithms course and understand a bubble sort is inherently slower than a quick sort, no amount of optimization will get you past the fundamental n^2 time complexity. You can generalize those same analysis principles to whole architectures and distributed systems. If you don’t recognize that the server interactions your solution requires boils down to an n^2 algorithm, you’re in for a bad time. Unlike a function a live, deployed system is typically harder to evolve. Sometimes constraints like backwards compatibility are such that you simply can’t fix it.

There's a different between fixing performance mistakes, which tend to be obvious in profiling as big hotspots, and designing for maximum performance, which requires that you start, really, with the specification of what your application is doing, then follow that through to architecture and design with a strong view to reducing unnecessary work and staying within the operations that can be done at high speed with current systems.