[goto11]

You approach scalability problems by identifying bottlenecks through measurements, and then you redesign the problem areas to solve the problem. This might be changing an inefficient algorithm, add caching, partitioning a database or whatever the problem calls for. Most likely the majority of code will be unaffected by these changes.

If an engineer propose that the only way to solve scalability problems is to rewrite everything from the ground up, it just tell you they haven't been able to identify the root cause of the problem. The rewrite will probably end up having he same problem.

[Silhouette]

Unfortunately that argument is like saying you approach security by identifying vulnerabilities and patching them or you approach performance by profiling to find hot spots and optimising them. Of course those things are often true in specific instances and that's usually where you should start.

However all of these are systemic issues and once you've picked the low-hanging fruit you can still be left with systemic problems that are not concentrated in one place but spread throughout your code. Eventually your profiler curve is nearly flat but your JavaScript or Python code still isn't going as fast as C or Rust. Eventually you think you've patched all of your injection points but if you're using manual string concatenation to build your SQL queries you'll probably keep missing others. And eventually you run out of places to add caches and load balancers and it turns out that your existing data storage model is fundamentally limited and needs to be replaced.

In each of these cases you may end up needing to rewrite a whole section of your application or a whole service in your distributed system because you can no longer paper over the cracks. Fortunately it happens relatively rarely but it certainly does happen!