dom-expressions VS countwords

I wonder how it compares to https://github.com/ryansolid/dom-expressions/tree/main/packa...

The template core, which is in https://github.com/ryansolid/dom-expressions/tree/main/packages/dom-expressions This template core manages the DOM and SSR-related APIs that is usually hidden from the user. This core is also "cloned" into the SolidJS repo via Rollup.

Revised my comment. However, I'm required to use Babel for: https://github.com/ryansolid/dom-expressions/tree/main/packages/babel-plugin-jsx-dom-expressions and a couple other small plugins.

Solid's transform seems fairly time consuming and difficult to maintain to me, though maybe it isn't, I'm not familiar with that code or with writing Babel transforms in general, you decide.

vuerx-jsx is using Vue's reactivity system (@vue/reactivity) with Solid's DOM renderer. Both offer blazingly fast performance, (much) faster than their original usage.

Solid is great, you can also use it with hyper dom expressions: https://github.com/ryansolid/dom-expressions

"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: