I can Port from Python -> Common Lisp at a rate of ~100 LoC per hour, and that's a pretty friendly port (the yield expression is the only thing that can't be just done line-by-line; truthyness is the only real "gotcha" as there are a lot of "false" values in Python, but only 1.5 in Lisp[1]).
1: I say 1.5 because there is only one false value, but it has 2 idiomatic meanings: nil (equivalent to python's None) and the empty list.
PyPy is faster than Python, yes. But Go, C and many other (compiled) languages are way faster than PyPy. Plus, if you use a language like Go or Rust then you avoid Python's GIL and you'll have much more reasonable memory usage. Best of all, deploying is a matter of copying a binary, rather than having to deal with the absolute disaster that is Python packaging.
> Plus, if you use a language like Go or Rust then you avoid Python's GIL
No, but then you run into Go's GC and green threads. File systems fit squarely in the realms of "systems programming" (old definition [1], not new). Languages like Ada, Pascal, C/C++, Rust and D (without GC).
I wouldn't consider the workstations sold by Xerox, TI, Connecting Machines, the OS research department at ETHZ or the Microsoft’s natural language search service for the West Coast and Asia, just experiments.
Maybe plenty fast for most applications but a filesystem is not one of these IMHO, especially for something as naturally resource hungry as ZFS.
A good filesystem implementation requires tight memory management and good control of what happens at the OS level. I am not saying it can't be be done in python, but it clearly isn't the right tool for the job.
I meant that for a production implementation. Python is perfectly fine for a proof of concept, in fact, it may be better than jumping straight down to C. But keeping it for production is foolish IMHO.
I was trying to speed up a log processing service running on PyPy by rewriting it in Java. I was surprised that the result was about twice slower (I know Java quite well and I didn't see obvious optimizations; most of the time was spent in GC). So it can be quite fast even in more absolute terms (VM languages), at least for some types of code.
I had a binary parser written in Python that took around 30 seconds on typical input on CPython. PyPy took that down to about 10 seconds. Rewriting it in C# took it down to 200 ms.
I routinely fail to get speedup on PyPy. In fact I frequently get slowdowns. I imagine it's only fast if your code is slower than it needs to be to begin with.
It works well for tight loops processing much data, or heavy object-orientation (multiple levels of class hierarchies). It probably won't work well for regular Django webapps or scripts. Also, real-world Python numerical/AI code uses numpy/ML libs so there's not much to optimize in Python...
If you're going to run the file system in user space, there's no reason not to just use normal ZFS. The problem with ZFS licensing is only in combining CDDL+GPL in one unit. If you're working across the kernel/userspace boundary, there's already no problem. ZoL even already ships a fuse version that works fine.
You'd want a reverse-engineering lawyer, so be certain. But my (IANAL) guess is: If this is a proper reverse-engineered implementation, you could then convert _this_ implementation to C, and contribute _that_ into the kernel.
Except, it seems this is BSD-licensed, so I'm not sure how that would work in the kernel (which is GPLv2).
I assume you mean this as a joke, but I would point out that at least one of the BSD family has gone and baked lua into their kernel. Granted, lua is rather meant for that kind of thing and python isn't, but it is entertaining to point out an interpreted language that has been stuck into a unix kernel:)