bu VS rules_python

I think Nim is great for small CLIs. Some examples are over at: https://github.com/c-blake/bu . To quantify "small", using tools themselves in bu/ (and Zsh *):

    wc -l --total=never **.nim|cols 1|cstats ms q.05 q.95

200 lines of Nim [1] seems to run about 9X faster than the 8000 lines of C in fdupes on a little test dir I have. If you need C, I think jdupes [2] is faster as @TacticalCoder points out a couple of times here. In my testing, `dups` is usually faster than `jdupes`, though.

[1] https://github.com/c-blake/bu/blob/main/dups.nim

[2] https://github.com/jbruchon/jdupes

You better off with using a compiled language.

If you interested in a language that's compiled, fast, but as easy and pleasant as Python - I'd recommend you take a look at [Nim](https://nim-lang.org).

And to prove what Nim's capable of - here's a cool repo with 100+ cli apps someone wrote in Nim: [c-blake/bu](https://github.com/c-blake/bu)

20 milliseconds? On my 7 year old Linux box, this little Nim program https://github.com/c-blake/bu/blob/main/wsz.nim runs to completion in 275 microseconds when fully statically linked with musl libc on Linux. That's with a stripped environment (with `env -i`). It takes more like 318 microseconds with my usual 54 environment variables. The program only does about 17 system calls, though.

Additionally, https://github.com/c-blake/cligen makes decent CLI tools a real breeze. If you like some of Go's qualities but the language seems too limited, you might like Nim: https://nim-lang.org. I generally find getting good performance much less of a challenge with Nim, but Nim is undeniably less well known with a smaller ecosystem and less corporate backing.

Often whole program generation in a prog.lang (& ecosystem!) that you already know can substitute for a new prog.lang. Python even has eval. You may be interested in: https://github.com/c-blake/bu/blob/main/doc/rp.md

You can actually get pretty far depending upon boundaries with the always implicit command-option language (when launched from the shell language, anyway). For example, Ben's example can be adapted to:

    rp -m^\[A-Za-z\] 'echo nr," ",s[1]'

You might consider: https://github.com/c-blake/bu/blob/main/doc/cols.md

That's in Nim, though that may not be much a barrier. (There may also be other tools in bu/ of interest.)

This sounds like a job for what standard C calls "popen". You can do `import posix; for line in popen("ls", "r"): echo line` in Nim, though you obviously need to replace `echo line` with other desired processing and learn how to do that.

You might also want to consider `rp` which is a program generator-compiler-runner along the lines of `awk` but with all the code just Nim snippets interpolated into a program template: https://github.com/c-blake/bu/blob/main/doc/rp.md . E.g.:

    ls -l | rp -pimport\ stats -bvar\ r:RunningStat -wnf\>4 r.push\ 4.f -eecho\ r

Nim is terse yet general and can be made even more so with effort. E.g., You can gin up a little framework that is even more terse than awk yet statically typed and trivially convertible to run much faster like https://github.com/c-blake/bu/blob/main/doc/rp.md

You can statically introspect code to then generate related/translated ASTs to create nearly frictionless helper facilities like https://github.com/c-blake/cligen .

You can do all of this without any real run-time speed sacrifices, depending upon the level of effort you put in / your expertise. Since it generates C/C++ or Javascript you get all the abilities of backend compilers almost out of the box, like profile-guided-optimization or for JS JIT compilation.

I don't know about all your other questions, but the https://github.com/c-blake/cligen CLI framework seems much lower effort / ceremony than even Rust's `argh` and is just about as old as `clap` (both started 8 years ago in 2015).

There are over 50 CLI utilities at https://github.com/c-blake/bu, many of which do something novel rather than just "re-doing ls/find/cat with a twist". While they are really more an "ls/ps construction toolkits" with some default configs to get people going, I think https://github.com/c-blake/lc and https://github.com/c-blake/procs are nicer than Rust alternatives. I mention these since you seem interested in such tools.

You are welcome. Thanks are too rarely offered. :-)

You may also be interested in word stemming ( such as used by snowball stemmer in https://github.com/c-blake/nimsearch ) or other NLP techniques, but I don't know how internationalized/multi-lingual that stuff is, but conceptually you might want "series of stemmed words" to be the content fragments of interest.

Similarity scores have many applications. Weights on graph of cancelled downloads ranked by size might be one. :)

Of course, for your specific "truncation" problem, you might also be able to just do an edit distance against the much smaller filenames and compare data prefixes in files or use a SHA256 of a content-based first slice. ( There are edit distance algos in Nim in https://github.com/c-blake/cligen/blob/master/cligen/textUt.... as well as in https://github.com/c-blake/suggest ).

Or, you could do a little program like ndup/sh/ndup to create a "mirrored file tree" of such content-based slices then you could use any true duplicate-file finder (like https://github.com/c-blake/bu/blob/main/dups.nim) on the little signature system to identify duplicates and go from path suffixes in those clusters back to the main filesystem. Of course, a single KV store within one or two files would be more efficient than thousands of tiny files. There are many possibilities.

What's SV?

I honestly don't know why anyone would use that... as in what does Bazel do better than virtually anything else that can provide this functionality. But, I used to be an ops engineer in a big company which wanted everything to be Maven, regardless of whether it does it well or not. So we built and deployed with Maven a lot of weird and unrelated stuff.

Not impossible, but not anything I'd advise anyone to do on their free time.

Specifically wrt' the link you posted, if you look here: https://github.com/bazelbuild/rules_python/blob/main/python/... it says that only pure Python wheels are supported, but that's also a lie, they don't support half of the functionality of pure Python wheels.

So, definitely not worth using, since lots of functionality is simply not there.

Regarding bazel, the rules_python has a py_wheel rule that helps you creating wheels that you can upload to pypi (https://github.com/bazelbuild/rules_python/blob/52e14b78307a...).

If you want to see an approach of bazel to pypi taken a bit to the extreme you can have a look at tensorflow on GitHub to see how they do it. They don't use the above-mentioned building rule because I think their build step is quite complicated (C/C++ stuff, Vida/ROCm support, python bindings, and multiOS support all in one before you can publish to pypi).

Python support in Bazel now looks more promising with `rules_python`: https://github.com/bazelbuild/rules_python

`rules_go` to my understanding is great too.

Over years, Bazel is not as opinionated as before, mostly because adoptions in different orgs force it to be so.

I have personally run converted build systems to Bazel, and use it for personal projects as well.

Bazel 1.0 was released in October 2019. If you were using it "a few years ago", I'm guessing you were using a pre-1.0 version. There's not some cutoff where Bazel magically got easy to use, and I still wouldn't describe it as "easy", but the problem it solves is hard to solve well, and the community support for Bazel has gotten a lot better over the past years.

https://github.com/bazelbuild/rules_python

The difficulty and complexity of using Bazel is highly variable. I've seen some projects where using Bazel is just super simple and easy, and some projects where using Bazel required a massive effort (custom toolchains and the like).

load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive") #------------------------------------------------------------------------------ # Python #------------------------------------------------------------------------------ # enable python rules http_archive( name = "rules_python", url = "https://github.com/bazelbuild/rules_python/releases/download/0.2.0/rules_python-0.2.0.tar.gz", sha256 = "778197e26c5fbeb07ac2a2c5ae405b30f6cb7ad1f5510ea6fdac03bded96cc6f", )