[VectorLock]

Typically large code bases have problems that are more intrinsic to organization than what language its in. Huge code cases in any language are usually fine if discipline is exercised.

And concurrency issues and memory overhead problem are from not knowing the right way to do it. Although I will concede that naive approach to both of those is sub-optimal.

[cwalv]

> Although I will concede that naive approach to both of those is sub-optimal.

Paradoxically, this can actually lead to a better understanding of how to write performant code. When I first learned some of the be implementation details of python, I couldn't believe it was performant enough to work for anything ... after optimizing enough of it, I understand better what actually matters

[VectorLock]

Also people seem to forget the rule of premature optimization. Worry about performance when performance starts to be a problem. You identify the area with performance is lacking and optimize that. Compute is cheap, manpower is expensive.