open_safety VS countwords

Malware delivered as an email with a link to a zip file containing a .js file is one of the most common methods of delivery, right behind word macros. The "map the .js extension to notepad.exe" is a common security trick with a measurable, immediate drop in malware in large orgs. You can deploy it via GPO or InTune.

Personal promotion, I built this as a better alternative:

https://github.com/technion/open_safety

Note the built in .js parser hasn't basically ever updated, if you're writing for this you're writing like you're targetting IE5.

Allow me to reference my own workaround for those vectors:

https://github.com/technion/open_safety

https://github.com/technion/open_safety

The time I've spent on the Github actions is substantively higher than the time I've spent on the .rs files. Of course you can't "test actions before commit" in the way you can actual code, so I kept having to make branches, make 15 commits like "try action fix again", followed by squashing them all down and merging.

"dang, I didn't know that was 50x faster than the idiomatic way" or "hey, I didn't know that this implementation in the stdlib prioritized this over that and made this so slow, that's interesting" -- .e.g, there's some kinda neat language details to be found in something like Ben Hoyt's community word count benchmarks repo and 'simple' vs 'optimal' code: https://github.com/benhoyt/countwords

It benchmarks programs that count the total number of unique words in some input. It's not exactly equivalent to your problem, but it's similarish. All of the programs used some kind of hash map for lookups, but I contributed a program that used a trie. Its performance in my experience varies depending on the CPU interestingly enough. On my old CPU (i7-6900K) it was a little slower, but on my new cpu (i9-12900KS) it was faster.

Why not read the source code? :-)

I wrote comments explaining things: https://github.com/benhoyt/countwords/blob/8553c8f600c40a462...

My own code example of a drastic speed up (~25%) simply replacing a couple of for loops with iters: https://github.com/benhoyt/countwords/pull/115

And yet Go was faster than Rust in a simple app that count words: https://benhoyt.com/writings/count-words/

> but the performance profiles & characteristics that we must know about in order to make a choice on which tool to use. And it shouldn't be that each user has to figure it out on their own, dig into PR's or whatever.

That's an interesting take – I like the idea of a catalog of standard tasks with implementations in several languages as well as their performance characteristics. I suppose Rosetta Code gets the ball rolling with this, but it's missing some performance metrics. It reminds me of [Ben Hoyt's piece](https://benhoyt.com/writings/count-words/) on counting unique words in the KJV Bible in different languages.

This article shows that map lookups can be optimized by using the (unintuitive) pattern: