No, if I have stuff, and I fork(), you, my child process is expected to have a copy of that stuff in it's current state. However, the kernel lies to you. It delays copying the stuff until I write to it i.e until I need to see a new state. Hence, "copy-on-write".
In this case, I am the parent python process, you are the child processes started with the `multiprocessing` module, and the data is the numpy array.
The implication is that the changes made by the parent process to the numpy array post fork() won't be visible to the child processes.
Yes the state machine data structure are also used in regex engines. The author of the fst crate also created Rust's regex crate and the ripgrep [0] CLI tool.
The regex crate has multiple implementations of regex matching algorithms, which is exposed as a library [1]. The implementations are selected at runtime based on which is faster and works right for a given regex. See also [2]
No, it isn't. The only similarity between them is finite state machines. Cox's article is about regexes. The fst crate is a data structure for storing keys and optional values. Two completely different things.
IME it's a test in which it is easy to grade many people in a way that it is really unlikely that two people end up with the same grade. That is what leetcode achieves and that is really the majority of the reason it is used. So it is just very convenient for hiring. In India, we have companies coming to hire students from colleges, and as the first filter, they give you three leetcode questions, and average the number of test cases you passed in each of them and rank you based on that. I spoke to one of the companies, they said that they had _very_ few people that had the same score, and it was easy to choose within those small sets by reading their resume/whatever.
Yeah, I like doing this too. Not _always_, but for a few things. I use it to emulate partial functions.
In a show hosting/ticket booking app for example, I never want in any case user facing search/by-id endpoints to serve a show from 2 months ago. So I create a view `select * from shows where time > now`. I can now use this as a 'table' and apply more filters and joins to this if I wish.
I assume parallelizing latex builds of separate sections for their website. Like building out blogs/physics and blogs/cs from their latex equivalents in parallel.
My Dell optiplex 9020 all-in-one, a compatibility beast, has all the usb ports you can think of, hdmi, sd card, CD drive, VGA... And yes, support for PS2 keyboard and mouse.
It can run windows 11.
Got it in 2012 or so, still running strong with a memory upgrade from 4->12.
Best computer I've owned.
Easy to maintain, nice documentation about the hardware and solid as a rock.
How about running it inside a VM, they usually emulate PS/2 devices. Or are there similar debuggers natively in something like VirtualBox/VMware already anyway?
In this case, I am the parent python process, you are the child processes started with the `multiprocessing` module, and the data is the numpy array.
The implication is that the changes made by the parent process to the numpy array post fork() won't be visible to the child processes.