The port had been done in a weekend just to see if we could use Python in production. The C++ code had taken a few months to write. The port was pretty direct, function for function. It was even line for line where language and library differences didn't offer an easier way.
A couple of us worked together for a day to find the reason for the speedup. Just looking at the code didn't give us any clues, so we started profiling both versions. We found out that the port had accidentally fixed a previously unknown bug in some code that built and compared cache keys. After identifying the small misbehaving function, we had to study the C++ code pretty hard to even understand what the problem was. I don't remember the exact nature of the bug, but I do remember thinking that particular type of bug would be hard to express in Python, and that's exactly why it was accidentally fixed.
We immediately started moving the rest of our back end to Python. Most things were slower, but not by much because most of our back end was i/o bound. We soon found out that we could make algorithmic improvements so much more quickly, so a lot of the slowest things got a lot faster than they had ever been. And, most importantly, we (the software developers) got quite a bit faster.
This was particularly true for one of the projects I've worked with in the past, where Python was chosen as the main language for a monitoring service.
In short, it proved itself to be a disaster: just the Python process collecting and parsing the metrics of all programs consumed 30-40% of the processing power of the lower end boxes.
In the end, the project went ahead for a while more, and we had to do all sorts of mitigations to get the performance impact to be less of an issue.
We did consider replacing it all by a few open source tools written in C and some glue code, the initial prototype used few MBs instead of dozens (or even hundreds) of MBs of memory, while barely registering any CPU load, but in the end it was deemed a waste of time when the whole project was terminated.
Turns out the metrics just rounded to the nearest 5MB
The main lesson of the story. Just pick Python and move fast, kids. It doesn’t matter how fast your software is if nobody uses it.
Just write the parsing loop in something faster like C or Rust, instead of the whole thing.
You are not the same.
Pure speculation, but I would guess this has something to do with a copy constructor getting invoked in a place you wouldn't guess, that ends up in a critical path.
It's true that writing code in C doesn't automatically make it faster.
For example, string manipulation. 0-terminated strings (the default in C) are, frankly, an abomination. String processing code is a tangle of strlen, strcpy, strncpy, strcat, all of which require repeated passes over the string looking for the 0. (Even worse, reloading the string into the cache just to find its length makes things even slower.)
Worse is the problem that, in order to slice a string, you have to malloc some memory and copy the string. And then carefully manage the lifetime of that slice.
The fix is simple - use length-delimited strings. D relies on them to great effect. You can do them in C, but you get no succor from the language. I've proposed a simple enhancement for C to make them work https://www.digitalmars.com/articles/C-biggest-mistake.html but nobody in the C world has any interest in it (which baffles me, it is so simple!).
Another source of slowdown in C is I've discovered over the years that C is not a plastic language, it is a brittle one. The first algorithm you select for a C project gets so welded into it that it cannot be changed without great difficulty. (And we all know that algorithms are the key to speed, not coding details.) Why isn't C plastic?
It's because one cannot switch back and forth between a reference type and a value type without extensively rewriting every use of it. For example:
struct S { int a; }
int foo(struct S s) { return s.a; }
int bar(struct S *s) { return s->a; }
struct S { int a; }
int foo(S s) { return s.a; }
int bar(S *s) { return s.a; }
The usual strategy is to write a script then if it's slow see how you could design a program that would
The usual strategy in the real world is to copy paste thousands of lines of C++ code until someone comes along and writes a proper direct solution to the problem.
Of course there are ideas on how to fix this, either writing your own scripting libraries (stb), packages (go/rust/ts), metaprogramming (lisp/jai). As for bugs those are a function of how you choose to write code, the standard way of writing shell it bug prone, the standard way of writing python is less so, not using overloading & going wider in c++ generally helps.
Crazy how many stories like this I’ve heard of how doing performance work helped people uncover bugs and/or hidden assumptions about their systems.
They found that they had fewer bugs in Python so they continued with it.
I suspect it’s more likely to be something like passing std::string by value not realising that would copy the string every time, especially with the statement that the mistake would be hard to express in Python.
Would be kind of cool if e. g. python or ruby could be as fast as C or C++.
I wonder if this could be possible, assuming we could modify both to achieve that as outcome. But without having a language that would be like C or C++. Right now there is a strange divide between "scripting" languages and compiled ones.
> Algorithmic complexity improvements dominate language-level optimisations. Going from O(N²) to O(N) in the streaming case had a larger practical impact than switching from WASM to TypeScript.
Yet they still have chosen to put the “Rust rewrite” part in the title. I almost think it's a click bait.
It looks like neither is the "real win". both the language and the algorithm made a big difference, as you can see in the first column in the last table - going to wasm was a big speedup, and improving the algorithm on top of that was another big speedup.
Kinda is. We came up with abstractions to help reason about what really matters. The more you need to deal with auxillary stuff (allocations, lifetimes), more likely you will miss the big issue.
One thing I noticed was that they time each call and then use a median. Sigh. In a browser. :/ With timing attack defenses build into the JS engine.
Thanks for cutting through the clickbait. The post is interesting, but I'm so tired of being unnecessarily clickbaited into reading articles.
You still do get some latency from the event loop, because postMessage gets queued as a MacroTask, which is probably on the order of 10μs. But this is the price you have to pay if you want to run some code in a non-blocking way.
So this holds even for L = M. The speedup is not in the language, but in the rewriting and rethinking.
They say they measured that cost, and it was most of the runtime in the old version (though they don't give exact numbers). That cost does not exist at all in the new version, simply because of the language.
If they used raw byte structures, implemented the caching improvements on the wasm side, the copies might not be as bad.
But they still have an issue with multi-language stack: complexity also has a cost.
Python/C combo does not have this issue because you can work with Python types natively in C, but otherwise, this is a cross-language conversion issue, and not a Rust issue at all.
Edit: fixed phone typos
This new company chose a very confusing name that has been used by the Open UI W3C Community Group for over 5 years.
Open UI is the standards group responsible for HTML having popovers, customizable select, invoker commands, and accordions. They're doing great work.
Looks inside
“The old implementation had some really inappropriate choices.”
Every time.
That final summary benchmark means nothing. It mentions 'baseline' value for the 'Full-stream total' for the rust implementation, and then says the `serde-wasm-bindgen` is '+9-29% slower', but it never gives us the baseline value, because clearly the only benchmark it did against the Rust codebase was the per-call one.
Then it mentions: "End result: 2.2-4.6x faster per call and 2.6-3.3x lower total streaming cost."
But the "2.6-3.3x" is by their own definition a comparison against the naive TS implementation.
I really think the guy just prompted claude to "get this shit fast and then publish a blog post".
I understand your frustration with AI writing though. We are a small team and given our roadmap it was either use LLMs to help collate all the internal benchmark results file into a blog or never write it so we chose the former. This was a genuinely surprising and counterintuitive result for us, which is why we wanted to share it. Happy to clarify any of the numbers if helpful.
Additionally even after those options are exhausted, only a key parts might need a rewrite, not the whole thing.
However, I wonder how many care about actually learning about algorithms, data structures and mechanical sympathy in the age of Electron apps.
It feels quite often that a rewrite is chosen, because knowing how to actually apply those skills is the CS stuff many think isn't worthwhile learning about.
Never mind the age of Electron apps, even fewer care about those in the age of agents.
And those will still care about CS.
AFAIK, you can create a shared memory block between WASM <-> JS:
https://developer.mozilla.org/en-US/docs/WebAssembly/Referen...
Then you'd only need to parse the SharedArrayBuffer at the end on the JS side
So you're reinventing JSON but binary? V8 JSON nowadays is highly optimized [1] and can process gigabytes per second [2], I doubt it is a bottleneck here.
[1] https://v8.dev/blog/json-stringify [2] https://github.com/simdjson/simdjson
The most obvious approach would be to let LLMs generate code and render it but that introduces problems like safety, UI consistency and speed. OpenUI solves those problems and provides a safe, consistent and token optimized runtime for the LLMs to render live UI
> converts internal AST into the public OutputNode format consumed by the React renderer
Why not just have the LLM emit the JSON for OutputNode ? Why is a custom "language" and parser needed at all? And yes, there is a cost for marshaling data, so you should avoid doing it where possible, and do it in large chunks when its not possible to avoid. This is not an unknown phenomenon.
Anyway, Javascript is no stranger to breaking changes. Compare Chromium 47 to today. Just add actual integers as another breaking change, then WASM becomes almost unnecessary.
You could also try pretty fast fft: https://github.com/JorenSix/pffft.wasm
I didn't mind reading articles that are not about how Rust is great in theory (and maybe practice).
So it's more so a story about architectural mistakes.
That said, Rust does have real problems. Manual memory management sucks. People think GC is expensive? Well, keep in mind malloc() and free() take global locks! People just have totally bogus mental models of what drives performance. These models lead them to technical nonsense.
It was able to beat XZ on its own game by a good margin:
This is apparent. xz's own game is not "a specialized compression pre-processor for x86_64 ELF binaries.". xz's own game is a general-purpose compression utility suited for a range of tasks, not optimized for one ridiculously specific domain. Also, any compression benchmark really ought to include speed of de/compression, not only compression ratio, as compression algorithms occupy along a scale trying to maximize one trade-off or another.
btw goal of the project was not building a production ready solution. It was curious case of black box software development. Compression is great because input and output are precise bits. As for speed, I think it's comparable since it's using most of XZ infra anyways.
Claude tells me this is https://www.fumadocs.dev/
In their worst case it was just x5. We clearly have some progress here.
Rust.
WASM.
TypeScript.
I am slowly beginning to understand why WASM did not really succeed.
Not sold about the fundamental idea of OpenUI though. XML is a great fit for DSLs and UI snippets.
The primary motivation was speed and schema cohesion. We were running a JSON based format, Thesys C1, in production for a year before we realized we cannot add features fast enough because we were fighting the LLMs at multiple levels. It's probably too much to write in a comment but we'd like to write about the motivation and all the things we tried ona a separate blog soon
The other day, someone linked back to this 2018 post on finding a cache coherency bug in the Xbox 360 CPU:
https://randomascii.wordpress.com/2018/01/07/finding-a-cpu-d...
So much more genuinely engaging than any of the AI-“enhanced” sloppy, confused, trite writing that gets to the front page here daily because it’s been hyper-optimized for upvotes.
I don't think that's actually out yet, and more importantly, it doesn't change anything at runtime -- your code still runs in a JS engine (V8, JSC etc).