A while ago at $work, we had a
performance issue with one of our core Python libraries.
This particular library forms the backbone of our 3D processing
pipeline. It’s a rather big and complex library which uses NumPy and
other scientific Python packages to do a wide range of mathematical
and geometrical operations.
Our system also has to work on-prem with limited CPU resources, and
while at first it performed well, as the number of concurrent
physical users grew we started running into problems and our system
struggled to keep up with the load.
We came to the conclusion that we had to make our system at least 50
times faster to handle the increased workload, and we figured that
Rust could help us achieve that.