Agreed.
> and communicating across processes often requires making expensive copies of data
SharedMemory [0] exists. Never understood why this isn’t used more frequently. There’s even a ShareableList which does exactly what it sounds like, and is awesome.
[0]: https://docs.python.org/3/library/multiprocessing.shared_mem...
Spawning a PYTHON interpreter process might take 30 ms to 300 ms before you get to main(), depending on the number of imports
It's 1 to 2 orders of magnitude difference, so it's worth being precise
This is a fallacy with say CGI. A CGI in C, Rust, or Go works perfectly well.
e.g. sqlite.org runs with a process PER REQUEST - https://news.ycombinator.com/item?id=3036124
httpdito http://canonical.org/~kragen/sw/dev3/server.s (which launches a process per request) seems to take only about 50μs because it's not linked with any C library and therefore only maps 5 pages. Also, that doesn't include the time for exit() because it runs multiple concurrent child processes.
On this laptop, a Ryzen 5 3500U running at 2.9GHz, forkovh takes about 330μs built with glibc and about 130–140μs built with dietlibc, and `time python3 -c True` takes about 30000–50000μs. I wrote a Python version of forkovh http://canonical.org/~kragen/sw/dev3/forkovh.py and it takes about 1200μs to fork(), _exit(), and wait().
If anyone else wants to clone that repo and test their own machines, I'm interested to hear the results, especially if they aren't in Linux. `make forkovh` will compile the C version.
1200μs is pretty expensive in some contexts but not others. Certainly it's cheaper than spawning a new Python interpreter by more than an order of magnitude.