Facebook + Instagram (largest deployment of Django)
Spotify (millions of lines of Python)
Netflix
Dropbox (millions of lines of Python)
YouTube
Plenty more examples.
Facebook + Instagram (largest deployment of Django)
Spotify (millions of lines of Python)
Netflix
Dropbox (millions of lines of Python)
YouTube
Plenty more examples.
Instagram (Cinder), Google (Unladen Swallow) and Dropbox (Pyston) have also all experimented with heavy engineering investment trying to improve Python performance and only one of them (Cinder) has outcompeted the “just rewrite it in a faster language” strategy.
How is this an answer to the question?
... do I really need to explain this?
P used for something in places in parallel of hundreds of (several exotic, rarely used) technologies is not really predicting how much P was used without scientific and ML uses. What if P is mostly used for scientific and ML purposes in those places, or as an experiment, study, not necessarily being a backbone? Or with other secondary agenda, overstated, etc. The mere fact that P is used in a huge amalgam of A,B,C,... tells very little alone concerning the question. Almost nothing.
Yes, needs some unwrapping what you believe.
I would argue moving away from Python at global scale doesn't even negate my point. All products change with scale.