countwords VS countwords

"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:

See an example: https://github.com/kimono-koans/countwords/blob/master/rust/fast-simple/main.rs

this is truly 1978 all over again. No flame graphs, no hardware counters no bottleneck analysis. Using these 'optimizations' for job interviews is questionable at best.

[1] https://benhoyt.com/writings/count-words/

This might actually be interesting to you: https://benhoyt.com/writings/count-words/

In case anyone is interested, I did an optimized, but much more simple, Rust implementation just today[0], which is faster than the optimized implementation on my machine. No indexing into arrays of bytes, etc., no "code golf" measures.

Looks like idiomatic Rust, which I think is interesting. Shows there is more than one way to skin a cat.

[0]: https://github.com/kimono-koans/countwords/blob/master/rust/...