adix VS cligen

Note: Just concatenating the bibles keeps your hash map artificially small...which matters because as you correctly note the big deal is if you can fit the histogram in the L2 cache as noted elsewhere and this really matters if you go parallel where N CPUsL2 caches can speed things up a lot -- until* your histograms blow out CPU-private L2 cache sizes. https://github.com/c-blake/adix/blob/master/tests/wf.nim (or a port to your favorite lang) might make it easy to play with these ideas.

which is notably logarithmic - not unlike a B-Tree.

When these expectations are exceeded you can at least detect a DoS attack. If you wait until such are seen, you can activate a "more random" mitigation on the fly at about the same cost as "the next resize/re-org/whatnot".

All you need to do is instrument your search to track the depth. There is some example such strategy in Nim at https://github.com/c-blake/adix for simple Robin-Hood Linear Probed tables.

Knuth-McIlroy comes up a lot. Previous discussion [1]. For this example I can make a Nim program run almost exactly the same speed as `wc -w`, yet the optimized C program runs 1.2x faster not 3.34x slower - a whopping 4x discrepancy - much bigger than many of the ratios in the table. So, people should be very cautious about conclusions from any of this.

[1] https://news.ycombinator.com/item?id=24817594

[2] https://github.com/c-blake/adix/blob/master/tests/wf.nim

There is also generating the whole thing from a function signature (e.g. https://github.com/c-blake/cligen ) since then CLauthors need not learn a new spec language, but then CLauthors must add back in helpful usage metadata/semantics and still need to learn a library API (but I like how those two things can be "gradual"). It's a hard space in which to find perfection, but I wish you luck in your attempt!

cligen also allows End-CL-users to adjust colorization of --help output like https://github.com/c-blake/cligen/blob/master/screenshots/di... using something like https://github.com/c-blake/cligen/wiki/Dark-BG-Config-File

Last I knew, the argparse backing most Py CLI solutions did not support such easier (for many) to read help text, but the PyUniverse is too vast to be sure without much related work searching.

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.

If you find this article interesting and are curious about Nim then you would probably also be curious about https://github.com/c-blake/cligen

That allows adding just 1-line to a module to add a pretty complete CLI and then a string per parameter to properly document options (assuming an existing API using keyword arguments).

It's also not hard to compile & link a static ELF binary with Nim.. I do it with MUSL libc on Linux all the time. I just toss into my ~/.config/nim/nim.cfg:

    @if musl:  # make nim c -d:musl .. foo static-link `foo` with musl

Sure. No problem.

Even Windows has popen these days. There are some tiny popenr/popenw wrappers in https://github.com/c-blake/cligen/blob/master/cligen/osUt.ni...

Depending upon how balanced work is on either side of the pipe, you usually can even get parallel speed-up on multicore with almost no work. For example, there is no need to use quote-escaped CSV parsing libraries when you just read from a popen()d translator program producing an easier format: https://github.com/c-blake/nio/blob/main/utils/c2tsv.nim

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.

It's more that those tools were what come to mind when I specifically think of my exposure to the existence of rust. Its perhaps not that the tools were there, but that they were well known (and known for being written in rust).

Anecdatapoint - I've never heard of literally a single one of the utilities listed on the bu page.

Regarding cligen, right from the start clap wins on producing idiomatic output. Compare: https://github.com/c-blake/cligen#cligen-a-native-api-inferr...

    Usage:

cool example would be this which is a CLI generation library. It lets you describe command line interfacs simply using function signatures

>Does nim have anything as polished and performant as clap and serde?

"Polished" and "high quality" are more subjective/implicitly about adoption, IMO. "Performant" has many dimensions. I just tested the Nim https://github.com/c-blake/cligen vs clap: cligen used 5X less object file space (with all size optimization tweaks enabled in both), 20% less run-time memory for large argument lists, and the same run-time per argument (with march=native equivalents on both, within statistical noise). cligen has many features - "did you mean?/suggestions", color generated help and all that - I do not see obvious feature in clap docs missing in cligen. The Nim binary serde showing is unlikely as good but there are like 10 JSON packages and that seems maybe your primary concern.

More to add color your point than disagree (and follow up on my "adoption") - your ideas about polish, quality, docs, etc. are part of feedback loop(s) you mentioned. More users => Users complain (What is confusing? What is missing? etc.) => things get fixed/cleaned up/improved => More users. Besides "performant" being multi-dimensional, the feedback loop is more of a "cyclic graph". :-) While I probably prefer Nim as much or more as @netbioserror, I am not too shocked by the mindshare capture. It seems to happen every 5..10 years or so in prog.langs.

While many of your points are not invalid, tech is also a highly hype-driven & fad-driven realm. In my experience, the more experience with this meta-feature that someone has, the more skeptical they are of the latest thing (more rounds of regret, etc.). Also, that feedback graph is not a pure good. Things can get too popular too quickly with near permanent consequences. ipv4 got popular so quickly that we are still mostly stuck on it 40 years later as ipv6 struggles for penetration. Whatever your favorite PL is, it may also grow features too fast.

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.