Is slow because of both. Using bad languages is one form of bad design itself, in fact.
Too many abstraction layers
Both matter…
The title itself is provably wrong. Take the Matrix spec / protocol, install Synapse (written in Python) and watch your 16GB ram, 4 cors shit itself live under 0 load; or run tuwunel (written in Rust) and watch it using 100MB of RAM and a single core while handling a hundred users (Dendrite written in go was also great but it’s incomplete). Same for some Mastodon implementations, even Elixir wasn’t great and I was disappointed.
Now if you are building some ETL or a thin API wrapper, where the bottleneck is somewhere else like a DB or another API, then yes, don’t bother, Python would do.
I don’t think you’re making the kind of argument you intended to make. Those are two different programs with two different development teams behind them. The design behind them (the literal thing the article is talking about) are likely completely different from each other. Comparing the languages based on two different implementations of the same spec is a bit more than disingenuous. If you want to actually compare you need to recreate the same implementation as closely as feasible. Something like this https://youtu.be/WB7v-ezqcJM
Comparing the languages based on two different implementations of the same spec
I don’t think you realize how ridiculous this reads
I hate these kinds of comments that have no other purpose than to sling shit and start arguments.
Since this is a programming community (and giving you the benefit of the doubt, I assume you’re not a vibe coder), you know your argument is nonsense. Writing something in Python doesn’t automatically make it slow or use a lot of RAM or whatever. Synapse is shitty because Synapse is shitty. You can write slow and inefficient software in any language, and it feels really tedious to type this out because it’s obvious and common sense.
Your entire comment adds nothing, like 0 value. I provided two counter examples of verifiable, publicly available, familiar to tech people and programmers projects people can inspect now. Your comment doesn’t even bother to bring something real, an LLM could put together a better counter argument by actually inspecting the github repos.
Synapse is shitty
I get the feeling you are wasting everyone’s time and you have no idea what are you talking about. Show us your cooler projects?
You can write slow and inefficient software in any language
Yes, but you can’t write snappy fast software in any language, high level languages like python, lua and php carry alot of overhead from the get go, because of dynamic typing, heavy abstractions, and heavy garbage collectors.
Optimising too early is the root of all evil or something like that
What if the language has a bad design?
muh io bound
🤣
If the DB is IO-bound, and “slow languages” don’t matter because “IO bound”, then show me the DB you’re using implemented in a slow language.
It’s hilarious how op is being this stupidly unaware, even within his stupid false argument which is provided as a supposed given.
How are people seriously engaging with this blog fart full of false premises and contradictions?
Has everyone forgotten the art of creating or using performance profiling tools
I was in a meeting with a senior software engineer, who makes a shit ton more than me. While sharing my screen he saw flame graph I had for one program I was working on. They asked “what is that?”, I was taken back, and said it’s for looking at which functions are taking more time. Then I was asked “where did you find it?” I was shell shocked, didn’t know how to answer that. Similar things have happened before where I taught them certain things, and they go “great find”, like dude, I didn’t find it just now. That’s basic knowledge if you work with database.
I am not even a developer hire, I’m self learned, and because the company doesn’t have many developers they gave me some of the tasks and I’m doing it because it’s more fun than my job responsibilities, and when I don’t have enough tasks.
Ive used them to help a lot in the past, but they often didnt give enough detail. Like I would know the area something was a problem, but pinpointing exactly what it was was still up to me, and would take some time. It worked but it could have been better.
Turns out there was a library you can add which adds that extra missing detail, and with that it was possible to highlight the exact issue. Wish id known about that sooner. That was on me though.
Flame graphs have really only been a thing for the last decade or so. The free tooling for profiling code has been shitty and difficult to understand for most of my career. And most companies aren’t willing to pay for it if the product runs “good enough”. These days companies lock down their shit so tight that you can’t even install an unapproved app no matter how free or useful.
I think that’s true for many tools. Programming tools are the one thing that has most advancement because so many programmer are there to improve on them. And even if you don’t use it, not knowing about it completely makes it a bit odd. Although most tools I have used to make flamegraph were as easy as just running the program as a argument to another program that tracks the function calls.
You see, performance is rarely a talking point in retrospectives unless its egregious. If you say a piece of compute is struggling the solution is to always buy more compute. It is rare that anyone would think of optimization unless you’re writing libraries.
I think that’s one of the problem with people that are only of a “finish this task cheaply for now” mind.
Having something run in 0.1 second vs 1 second is fine when a user is running it for one thing. So they consider it good or instantaneous. And later they have to do it for 1000 other things, and they don’t think that saving time in that “instantaneous result” there would save so much more time. They just go “it’s 1000 of these so it takes time”. I’ve found functions that open a db file, read one entry and close it. Then that function is called for each entry they have to read later. In a preprocessing step where they know all the entries they need beforehand. And even the data tyoe to store it is nor preallocated, they just append it on every data read; again on a fixed number of entries they need.
I understand performance not being the most important thing, but I’d expect least a basic understanding of design that has unnecessary processing.
Or if its employee facing only let it get slower and slower to the point where it takes a minute to update one record
Which is exactly why western AI companies are bankrupting themselves building data centers while China is easily keeping pace with a fraction of the resources. They’re not magic. They just do basic things like make things more efficient.
Pay me to do it and I’ll gladly do it. Most companies, however, don’t really care much about whether the customers take an extra 100 milliseconds for every request or not. And when it’s promotion time, you’re often judged by how many tasks you get done, not how fast your code is.
Yes, people care way more about which cool new features were delivered than which technical debts were solved by a proposed initiative. Sometimes it’s frustrating to see that performance improvement work only gets prioritized after a big incident.
There are NP problems and you should know how to assess them.
I’d add that most of the time nobody actually measured anything. at work we inherit slow sites all the time and the first guess is always “the stack”, then you profile it and it’s one query running per item in a loop or something like that. I had the same with my own side project, was sure I needed a bigger server and after measuring the problem was somewhere else completely. measuring first is boring but it’s cheaper than any rewrite I guess
Profiling complex systems can be quite difficult. That said, I believe in the adage “Profile twice, optimize once.”, adapted from the old carpenters adage of “Measure twice, cut once.”
Slowness is rarely essential, but I can be far away from where you expect it.
That’s what Open Telemetry is for, honestly, specifically tracing. I’m in the process of adding tracing for all of our web handlers, http client calls, database transactions/individual queries, and asynchronous jobs. Should give us a pretty good view into the performance of the system, and allow us to monitor for things like performance regressions without any dedicated profiling needed (though we certainly could add additional profiling information through Otel down the road). Since I know you’re a Haskeller (recognize your handle), the
hs-opentelemetryecosystem is quite solid and has integrations with a lot of libraries in the wider ecosystem, likewai,persistent,postgresql-simple,conduit, and a bunch more. I haven’t needed to write too much of our own instrumentation, aside from our job system since that’s a bespoke system.Yeah we used hs-opentelemetry at my last paid position. The company was an MS shop, tho so we were using ODBC to connect to MS SQL Server which doesn’t have great in-process profiling. But, at least we could figure out what quer(y/ies) were running long and then we could use the MS tools to look at the plan and fiddle with that. It was also a small org, so it’s not like there was a lot of internal services in one flow – basically everything either stayed in the Web Yesod app or in the IVR app. All our profiling could have been instrumented process profiling instead of having to use otel at all.
So, I haven’t seen how easy/difficult it is to actually use otel across multiple internal service calls to satisfy one external request.
Noticeable speed? Sure. But I am so pissed if a fucking todo list app is a 100mb download and consumed 300mb memory when running, all because this fucker bundles and loads a whole browser engine to render itself.
I therefore do think tech stack matters as well.
Web development and using browser renderers for GUIs is one of my most hated aspects of modern software. 🤢
Electron should have never existed and should be cometeñy banned, yeah
Sorry guys, back in 2008, I asked a monkey’s paw for better first party software support for desktop Linux, and this is what we got.
I once did some testing comparing very simple apps built with Tauri (using system webview) and native library and the difference in memory usage was not that big. I’m looking now at a simple PDF viewer in GTK and it’s using 120MB. My app written in Tauri is using 150MB. In theory native apps will use less memory when you run a lot of them at the same time but if you check the size of native Linux app they are not that tiny. They still will easily use hundredths of MB of RAM. Simple, native todo app will easily use 150MB. Yes, badly written electron app will use way more than that but you have webview tools that are really good and not that heavy.
I’ve done tests like that too, and I can’t agree with you.
Plus, GTK is a bad comparison. One of the worst native toolkits, if not the worst one along with any and all immediate mode ones.
I’ve also compared native Rust library (egui? don’t remember exactly) with Tauri and the difference wasn’t that big. What did you compare? As I said, native solutions should use less memory if you run a lot of them at the same time as they will load shared libraries only once. So I guess if you run KDE and start single Qt app it will look better than running single Qt app under Awesome WM or cosmos.
I compared one single Qt app (QOwnNotes) with one single similar Tauri app (I don’t remember the name, I was trying apps because I was looking for something extensible that wasn’t Electron-based as Logseq). It was a huge difference for me, at least, more or less 100 MB, and for what I knew, it was not that badly programmed.
And for me, with only 16 GB lf RAM and an Intel Xeon v4 (12C/24T @ 2.20 GHz max) it’s even more noticeable, specially because I work in multimedia (music production and sound design) and I have to use a browser and a communications app all the time because I need to research stuff and communicate with some peers and my clients (the very few times I have clients).
Every byte counts, even if you have lots of memory and a huge processor and GPU. Thinking that “1 MB is not that much” is what has led us to this crysis. We should make any and all possible optimizations to be able to ship featureful software that’s not overbloated (Suckless dickheads can suck my ass).
Had the same experience when I was testing zed. Was thinking it would be a lot better since it’s written in rust but there wasn’t any difference in memory/CPU usage between it and VSCodium, which is electron app.
Even nvim will eat up about 5GB of RAM when used as an IDE. MCP alone will use that much of RAM independent of the editor.
From what I remember, Vscode has quite a fair bit of native code in various areas to make it perform better. So it may not be the representative Electron app for comparison.
Electron is also the worst web based toolkit out there. Solutions that use system webviews instead of running whole Chrome are much lighter.
hundredths of MB
That would be 20 kB, 30 kB, 40 kB, I don’t think you meant that.
Nah, software is slow because it runs a web engine for GUI.
I’d say a bit of both. People who are fine with having a DOM worth of overhead don’t give me much confidence they are selecting algorithms well, which can absolutely affect performance and efficiency regardless of what language they are written in.
Hell, even with the optimal data structures and algorithms selected, how you set them up can make a huge difference.
Like I once had essentially a data translator that, in order to be generic, used keys in a dictionary to replace the hardcoded data structure that needed to be updated any time the spec changed, even if we didn’t care about the new values.
The first version used an array of dictionaries and was slow as fuck. Each line of data would get its own dictionary and just parsing the data into this scheme took a couple minutes IIRC. Since the data was guaranteed to have the same keys each line (we were worried about changes between versions, not lines), I changed it to a dictionary of arrays, where each line would have the same index across the dicts, and it brought parse time down to several seconds. Huge difference even though big O notation would have simplified to the same expression for the two methods.
But if you pick something with a worse big O expression, it gets worse on a whole other level.
Why would that slow it down exactly? Nothing really slow about GPU accelerated graphics these days?
Would love to hear the technical reasons. You need to have UI kits for GUIs. Responsive and accessible UI kits are hard. The web solved them a decade ago and made them easier to achieve than ever before. Why invent the wheel twice?
I/O is the slowest thing ever. The more I/O dependent your app is, the slower it will be. It is not easy to make a slow web UI with zero I/O. Very easy to make a slow UI with a native toolkit and high I/O usage. Network calls are I/O. Analytics are I/O. Most enterprise apps these days demand so much analytics they effectively have spyware on their own users to optimize and A/B test against.
It’s mostly because of memory cache misses. JS creates a ton of closures and has a lot of context switching. JS engines are basically a modern day miracle for how fast they are but somehow webapps are still sluggish.
Memory cache hits are destroying performance? Is this profiled or just guesswork? I don’t understand how that’s tied to perceived performance degradation specific to web-based toolkits like Blink and Webkit
It still adds a lot of layers between the user and the processor.
What does that even mean? I make GUIs in web technologies, I make GUIs in godot, I make GUIs in bevy, I make GUIs in flutter, etc etc. Blink uses Skia. Flutter uses Skia. Is skia just bad? What are you even saying? What is slow about web tech, really? To me performance is almost always bad engineering choices like not understanding or handling I/O.
The issue is that any time you add another generic layer, it involves another layer of loops for “handle everything” tasks and lookups for “handle this specific thing” tasks. A native app could set a bunch of values for properties by changing them in a single function accessing a single data structure. You might have a single function that does the same thing using frameworks, but that function is potentially going to use accessor functions to reach down each layer to set those values where they matter plus could need to do a bunch of transformations as it traverses each layer (or maybe that’s done on the render side).
So then you add a bunch of caching to improve these common scenarios and balloon memory usage, which was already inflated because those layers also need memory to store their data structures.
And these things get worse as they scale, so small scale tests still look snappy and hide how much things can grind to a crawl when it needs to handle the large or normal scale. Not to mention, if you don’t have a speedy version to compare to, you might just think that it makes sense for this process to take a couple minutes because it has millions of things to do (despite running on hardware that can do billions of things per second).
To me performance is almost always bad engineering choices like not understanding or handling I/O.
So that means every single developer that has ever used Electron or CEF for something, has been making bad engineering choices?
You may have something there.
Why is how nodejs or cef does I/O worse than any other GUI toolkit’s I/O layer?
I haven’t gotten deep enough into it to see what’s wrong, but pretty much any Electron or CEF application I’ve used has been pretty slow, though it could be that I have a very weak PC since I only have a Ryzen 5700 and 32 gigs of DDR4 and my NVMe drives (root and home partitions being separate physical drives) are only running on PCIe 3.0, not 4.0 or 5.0. Perhaps I ought to see if things like Steam or Discord run better if I upgrade to a mid-range PC with a Ryzen 9900x, faster RAM and faster drives, but I’m not made of money.
Games run smoothly, mind you - those don’t require very beefy hardware after all. It’s only extremely visually and IO demanding applications like Steam or Discord that run like absolute dogshit for me. Slack used to be pretty bad too, but I don’t use it these days, don’t need it. Ditto for Mattermost. I have one computer dedicated to Microsoft Teams, it’s got a 12th gen i7 in it which can’t really handle the task, but that’s just Microsoft for you, everything they’ve made in the last decade has been shit.
“layers” don’t necessarily make things appreciably slow.
God, this again.
I worked at a place where a site had to show the number of members, but for some reason it didn’t show the correct number. The reason was the query to the database was wrong, I corrected it in five minutes and the boss told me to put numbers manually, increasing them until we could display the correct query result because apparently the problem was there a long time before I started working there and it would make a bad image for the company if it was so easily corrected.
On other project a friend of mine told me about things were so slow and getting slower with time. Database didn’t have keys, indexes, nothing you should expect a relational database to have.
I haven’t worked in a while because of my health issues and I can assure you I’d be happier getting paid for watering plants than working with computers.
I wrote a program in Python to read some huge Json files. It took forever to finish. Turned out I could optimize it with some simple changes, which reduced 10 minutes to 10 seconds (not literally, just figurative) and made a difference in huge files only. Later I rewrote the program in Rust. And you know what happened? It was slower than the Python version. Only after understanding what I was doing and making right changes made the Rust version faster.
Most of the time its the design of the code or libraries that slows down. The language might play a huge role in performance, but it depends on what type of code and application it is.
I actually wanted write a blog post about this… if I still have the project data I might explain what happened. BTW this had nothing to do with Ai.
As an OPS guy you know what really grinds my gears? Developers not paying attention to shit like this.
We have a job in production that is a powershell script calling a powershell script calling a java applet. Now you may already be thinking “what is this abomination?” And you’re right, but that java application is set to DEBUG. IN PRODUCTION. So every time it runs it generates 1gb of logs.
Do they fix it? No of course not because if it’s not broken don’t fix it. But Jesus Christ we have people developing without understanding fundamentals like “YOU DONT LEAVE DEBUG ON IN PRODUCTION”.
I don’t understand how this is even possible.
People are dumb
yeah, sometimes it feels like the same people complaining about python speed are the ones writing “C” code in python.
What about the fact that runtime startup alone auto-disqualifies python for all short-lived process use-cases?
Or the fact that you can’t do concurrency especially across the language barrier from another language without multiple python runtimes orchestrated with a queue and IPC because of possible process-wide global state and similar concerns/assumptions for any python library dependency that is not all native across its own dependency chain?
“languages are just tools” and “use the right tool for the job” were already stupid catch phrases. Pretending languages don’t matter is a big further step into this shallow faux-intellectualism masquerading as wisdom.
And we didn’t even get to low level stuff yet.
What about the fact that runtime startup alone auto-disqualifies python for all short-lived process use-cases?
While Python’s start-up time is a lot slower than a corresponding compiled program, in most cases it’s not slow enough to matter even if the process itself is short-lived:
$ hyperfine 'python3 -c "pass"' Benchmark 1: python3 -c "pass" (412 runs) mean ± σ min … max Wall Time 7.1 ms ± 0.2 ms 6.7 ms … 8.4 ms Memory 9.4 MiB ± 0.1 MiB 9.1 MiB … 9.5 MiBIf you are running a small program so often that this kind of start-up time matters, then the loop should probably already be implemented in the program itself. In line with the article’s thesis
Thanks for putting numbers. People really overblow python performance issues when it literally does not matter most times.
Yeah nah, Lua runs circles around Python both at runtime and especially at startup. I’ve made me some desktop automation scripts in Lua to call from Alfred, I had to recheck that I didn’t leave static test data in there instead of calling the script, because the results were appearing instantly.
Recently had to develop a Python project, the tests took at least ten seconds to start each single time. This is while I made over a hundred tests for my module and re-ran them after every change.
Recently had to develop a Python project, the tests took at least ten seconds to start each single time. This is while I made over a hundred tests for my module and re-ran them after every change.
Python is dog slow, but if your tests are taking 10 seconds to start (not even run!), then something is catastrophically wrong with your setup. For comparison, for one of my projects, it takes a total of 12 seconds to run nox on a clean slate, which involves setting up venvs for five major versions of Python, installing the package and its dependencies in each, and finally running the 1500 test test-suite five times
It wasn’t my project. Afaict it’s a standard pytest setup with the Litestar framework (unused in my tests) and an in-memory mock Postgre transaction with like five records per test, so choose for yourself which part of this is ‘catastrophically wrong’. However, I can indeed agree that most of it is trash.
Same approach with dozens upon dozens of tests worked snappily as heck in PHP, haven’t yet tried it with my beloved Lua.
Then its the Test environment that is slow, not Python.
Lua also doesn’t provide everything Python does and has a totally different use case.
Oh really? Well guess what, that’s a huge benefit for Lua because its entire total size is around 500 KB. It’s loaded into memory and executed start to finish faster than you blink. If you need a ‘standard library’, just use Penlight. As I already mentioned, vast majority of libraries for Lua are written in… Lua itself, because it’s so goddamn fast. I’m using Fennel, the Lua Lisp that’s transcribed on-the-fly, and it’s still faster than Python. Lua is great for interactive tasks like desktop automation, e.g. with Alfred, Hammerspoon (which latter uses Lua throughout), or for scripting in Termux on the phone where Python would kill the memory immediately.
Lua’s problems with the absence of popular libraries aren’t because of any language issues (even the lack of
nullcan be dealt with), but solely due to its non-adoption in the masses. It is very popular as an embedded language in gaming, though, and that’s not a coincidence, because it fits that niche precisely because of its small size and extraordinary speed.Well guess what, that’s a huge benefit for Lua because its entire total size is around 500 KB.
Filesize doesn’t matter, if the functionality is missing. Lua is just not a direct comparison and replacement for Python. Penlight isn’t standard library. In example if I want to write a commandline script, I better off doing it in Python. The language is much better to me, has better features, has a standard library builtin and is even installed in all Linux distributions.
Lua is meant to be embedded into other applications, not meant to be used on its own.
Lua’s problems with the absence of popular libraries aren’t because of any language issues (even the lack of null can be dealt with), but solely due to its non-adoption in the masses.
Wrong. This is a design choice, because Lua isn’t meant to be used by its own. The embedded environment is meant to provide the functionality. In example in Neovim they provide their own functionality embedded within the editor. I can’t use that script on a different environment.
Or the fact that you can’t do concurrency especially across the language barrier from another language without multiple python runtimes orchestrated with a queue and IPC because of possible process-wide global state and similar concerns/assumptions for any python library dependency that is not all native across its own dependency chain?
This is very out of date. I haven’t written Python for over 4 years and even back then, I was using multiprocessing.Pool for pretty much all parallel processing that I ever needed. Sure, it’s doesn’t fit every use case for concurrency, but for simple stuff it’s perfectly fine.
Not only that, it’s one of the simplest parallel processing APIs I’ve ever used:
from multiprocessing import Pool def f(x): return x*x if __name__ == '__main__': with Pool(5) as p: print(p.map(f, [1, 2, 3]))It’s not outdated, and maybe you didn’t get what I was talking about, but probably because I didn’t word it well. So let me quote someone else instead:
While pure Python code should work unchanged, code written in other languages or using the CPython C API may not. The GIL was implicitly protecting a lot of thread-unsafe C, C++, Cython, Fortran, etc. code - and now it no longer does. Which may lead to all sorts of fun outcomes (crashes, intermittent incorrect behavior, etc.)
This covers the layers below python, but still doesn’t fully cover the process wide restrictions (it’s not just about thread safety, global initialization and resources are very relevant too). It also doesn’t cover the exact same problem which also exists for any layer calling into python from above, which also relies/relied on the GIL for synchronization. This means that you can’t effectively, confidently, and safely scale python without scaling the number of python runtime instances, each in its own full OS process.
The fact that
multiprocessingbecame popular, instead of just scaling/parallelizing with threads, is itself an extension of the GIL/no-GIL circus. But its not the primary point here.So not only the proposition (which someone referenced in another sub-thread) to move everything into python is in and of itself a self-defeating argument, since it proves a language being a problem (through forcing language lock-in). But even within a language (python), not all problems are solvable still.
Note that this is not all theoretical for me. In one of my previous projects, I had to call into a python library (which didn’t have an adequate alternative) from rust code (through pyo3). And I was forced to scale with multiple python runtimes, each getting one request/message at a time.
And we still didn’t get to any low-level details yet 😉
Ah okay, I see what you’re saying. GIL removal can break FFI code through the C API.
I have a lot of thoughts on this, and most of it is too incoherent to put into a comment someone else can understand, but for the most part, I think the issues are still pretty overblown. I just can’t see a world where Python needs true multithreading support when multiprocessing works just fine for most applications. You find a hot loop, you parallelize it in multiprocessing and just like that, your process runs much faster.
I do think that GIL removal is a terrible idea in general, for the reasons in the quote you provided, but at least it’s optional. I would personally not enable it in anything I maintain for several years if I didn’t have to.
I also think that polyglot code running in the same process is also a bad idea. I can understand calling C or Fortran from Python for numerical processing for example, but doing the reverse (like calling Python from Rust) is something I try to avoid at all costs. In any project I’ve worked on, if significant portions of the codebase were written in different languages, you would just use something like protobuf to specify the I/O protocol between the different languages and call them across a socket. It’s easier to maintain in my opinion since it defines a concrete boundary between the two different portions of code, and also makes it much easier to test each component separately too.
If you didn’t automatically register that requiring spawning full OS processes to scale performance immediately disqualifies a language from a whole plethora of use-cases (which is what the intellectually deficient OP tried to argue against), then I’m not sure you’re qualified to opine on the matter (hint: spawning a thread per task is already too much overhead for many workloads). And I don’t mean any disrespect to you here. Your replies were far from the worst in this thread.
Frankly this thread has been very illuminating. My perception of the average p.d user and their concrete knowledge was very inflated.
And we didn’t even get to low level stuff yet. People are already failing at the macro level. Sad.
If you didn’t automatically register that requiring spawning full OS processes to scale performance immediately disqualifies a language from a whole plethora of use-cases
That’s way too broad of a statement to be true in any absolute sense. It really depends on what you’re doing, and sure, there are plenty of ways to do it stupidly, but a lot of people that are smarter than you and me have thought about ways that the overhead of spawning processes can be mitigated. All I can say is that I’ve had perfectly good success using
multiprocessing.Pooland getting near perfect speedups. Is it as fast as threading? No. But it’s good enough. If it’s not good enough for you, then maybe we just have different use-cases, but without talking about concrete examples, we can’t make any specific claims.And we didn’t even get to low level stuff yet. People are already failing at the macro level. Sad.
What do you want to talk about? Are you writing kernel drivers in Python or something? Why would we talk about writing low level code in Python, that sounds absurdly stupid. Most of the high performance libraries people use are written in Fortran or C. The entire field of data science uses Python, if performance were not a heavily optimized problem, then a lot of people out there must really love wasting their money on compute. Python is a glue language for high performance low level code, everybody knows this already.
Frankly this thread has been very illuminating. My perception of the average p.d user and their concrete knowledge was very inflated.
At least we agree on something!
At that point just call the person messy and move on. They just want to flex their brain.
Counterpoint, I wrote a Python program that reads a load of JSON and runs regex searches over it. It was quite slow. Rewrote it in Rust as a straight 1:1 port, no algorithmic changes. 50x speedup.
I think you must have either done something very wrong or your Python program didn’t really do anything other than call Python built-ins that are actually written in C.
Did you use a profiler to find the bottlenecks? Did you try using cjson to speed up the json parsing? I would also avoid using the stdlib regex library too. It’s probably more of a convenience/reference implementation, not something to be intended for high performance. Maybe there’s a re2 wrapper for Python or a similar regex library in C with Python bindings.
Rewriting in Rust is fine, but good Python devs know how to find bottlenecks in their software and replace them with fast implementations. I’ve written plenty of performant Python code, it’s not very hard to profile and optimize. Usually the performant stuff is already written in C/C++ or Fortran anyway (numpy, PyQt, etc.)
Did you use a profiler to find the bottlenecks?
No. It was a simple enough program that there were no areas for improvement anyway. I also did not use a profiler for the Rust code.
good Python devs know how to find bottlenecks in their software and replace them with fast implementations
In my experience this is also a sweet sounding lie to make people feel ok with using Python. Yes in some case you make a big mistake and there’s a large bottleneck you can easily remove… Or your program has a nice small hotspot where it spends most of its time and you can just write that bit in C++ (AI is the obvious example).
But most programs are not like that. They’re more like MS Word. Slowness is evenly distributed through the code. There is no hotspot to remove.
As an example of this, look at Nicholas Nethercote’s excellent series of blogs on making the Rust compiler faster: https://nnethercote.github.io/
The “bottlenecks” they find result in 1% speedups.
If you didn’t use a profiler, then how can you be sure that the issue was Python and not something else? If the slowness really is evenly distributed through the code as you suggest, then a profiler would show that almost instantly and it’s very easy to do that in Python using cProfile.
On top of that, the Python standard library is built for convenience, not performance. The nature of it being a standard library means that the implementation has to be simple. fastregex uses LLVM and SIMD for example, that’s something that’s wildly impractical for the Python stdlib to support. In contrast, I have no idea what regex or JSON library you used for your Rust code, but if it were outside the stdlib, chances are high that it had much more leeway to optimize performance because it didn’t have all the other maintenance obligations that stdlib code needs to support.
All of this is just pure speculation on my part, but ultimately I’m trying to make a point that it is hard to say that the difference was caused by a language issue and not something else. There are lots of other things that could be contributing to this difference and you can’t really say for sure without meaningful diagnostics.
As an example of this, look at Nicholas Nethercote’s excellent series of blogs on making the Rust compiler faster: https://nnethercote.github.io/
The “bottlenecks” they find result in 1% speedups.
The kinds of software that you and I write on a daily basis is nothing compared to pieces of software that get millions of man-hours per year. Assuming you’re not Linus Torvalds or Ted Ts’o hiding behind a funny alias, of course. I don’t think anyone is suggesting that it’s a good idea to write a compiler in Python (outside of prototyping purposes perhaps) since it’s obviously not I/O bound, but if you and I are trying to optimize something we wrote ourselves, we’re more likely to be in the “nice small hotspot” domain you described, rather than the 1% bottleneck speedups discussed in the link you posted.
Very cool link though by the way, I look forward to reading as much of it as I can…
If you didn’t use a profiler, then how can you be sure that the issue was Python and not something else?
It was a relatively simple program and I am good at programming.
The point is, I put the same amount of effort into writing the same program in Python and Rust, and Rust was 50x faster. With AI, I don’t see any reason to use Python these days anyway. I never bought the productivity arguments before (Rust is easily as productive as Python), but with AI Rust is definitely more productive than Python because of the much stronger static checks.
It wasn’t a very wrong what I did in Python. I did the obvious thing. While I do not remember details, I remember it had something to do with adding items I think. But sadly I have not documented this issue, that’s on me. It was only noticeable for really huge files like 50MBytes json. Edit: Most weren’t this big, but it also combined over 100 Json files into one, so a search would search them all through and be able to save as a single file.
I mean, python has a very nice set of tools that run at C speed so it’s not crazy to think you can get good performance out of it.
The thing is also that you can’t separate a language from its ecosystem.
Because it is possible to write the fastest programs in C/C++ and Rust, folks that actually need the fastest performance will use these languages and will go around opening pull requests to fix performance problems in commonly-used libraries.
So, you do often end up with the most performant design anyways, just because the language itself is fast on paper.
I wrote a draft of a service that would take hours to complete. when I optimized it for multithreaded operation instead of taking hours it took around 60 seconds.
write software like you write research papers. first draft > second draft > final draft > publication
I’d love that but it’s difficult when it’s not really my project and anything that is functioning well enough gets you assigned the next ticket at work.
anything that is functioning well enough gets you assigned the next ticket at work.
Ship in the minimum amount of time possible, by any means, to be first to market. And this means poor design and smooshing together all kind of fat general-purpose frameworks and libraries to get something out the door.
Sure, it’s slow. Google’s landing page is 4 MB for a small graphic, a text form, and some buttons. But what are you going to do? Not use it? They won. They don’t have to fix shit.
And now with LLMs they can produce bloat faster than ever and pay even less attention to speed or quality.
“The goal was never quality software.”
Sure, it’s slow. Google’s landing page is 4 MB for a small graphic, a text form, and some buttons. But what are you going to do? Not use it? They won. They don’t have to fix shit.
This neglects to include the massive amount of additional front-end code Google (and other large tech companies) toss on their webpages for things that are purely for their own benefit while being of zero or negative value to the users. Mouse tracking, analytics, fingerprinting, and all kinds of other harmful content that tech companies put out go a long way towards reaching that 4MB figure.
Google is a bad example, historically the landing page has been interestingly optimised.
Lots of corporate websites are multi-hundred MB things though you are right.
Edit I take it back, the worst ones I checked have been redesigned
I’d like you to name 1 that’s multi-hundred MB.
Can’t, I was thinking of MSN.com but they had a redesign sometime, they come in at 5.6mb now
I saw something where the NYT webside takes up over 100MB if RAM.
…I don’t use Goog’s search, just like probably many people on Lemmy.
Yeah, I long ago switched to ddg and I also turn off the ddg ai stuff. I hear the paid search is also out-performing Google for people that care their search results are accurate.
Never understood the idea of, “if it needs to be fast, that part is in a faster language.” Why not just use the faster language? Most of the libraries that have Python wrappers also come with c headers. Sometimes you wind up in the reverse situation where your dependency doesn’t have a Python wrapper and now you’re stuck managing silly boilerplate. Python just feels like an extra thing in the way to wrangle for little gain.
Also, hotspot-only optimization doesn’t seem to have held up in reality, right? We can all agree that software is getting slower every day. The infrastructure is slow.
All feels like cope, through and through.
Why not just use the faster language?
Because the other language is more developer-friendly or faster to write/read even if slower to execute. Some code doesn’t need to be fast because it is always waiting on other things anyway.
Some languages effectively prevent whole classes of runtime failures / bad code. But, they do that by doing more which takes longer, at compile-time, run time, or both.
Some programs can’t avoid graph-structured data dependencies. In that case, the simplest solution is a garbage collector, and one built into the runtime is capable of being more efficient (and less buggy) than one written for a specific program.
Anyway, there are many factors that determine which language is “best”, and not all of them are objective. That’s why we don’t just use the fastest language. (Currently, I do all my coding in either Idris or Purescript, but I’m also unemployed and just programing for myself, for now.)
IMHO it’s easier to quickly iterate with Python than with C++. If you aren’t even sure what you want and you’re just writing stuff, then Python is great. Is “4” true? Ofc it is.
All programming is a trade-off between what’s easy for the developer and what’s fast for the machine.
Sure, you could theoretically write everything in Assembly and it’d be blazingly fast; but unless you’re a particularly elite developer, that process would take forever for any program more complicated than a calculator, the result would be super buggy, and those bugs would be very hard to fix.
On the other end of the spectrum, you might imagine a natural language machine learning compiler which translated directly from human prose to binary. It would be dead simple to “program” anything in; but such an application would probably be unbelievably slow, and again, have a staggering number of difficult-to-fix bugs.
Somewhere in the middle are modern programming languages that abstract away all of the stuff that’s necessary but hard for humans to wrap their minds around (memory management, API interaction, network stack, etc) and let the human write clear and unambitious code to handle all of the business logic and displaying it to the user. The result won’t run as blisteringly fast as writing directly in Assembly, and it won’t be as quick to market as Vibes-to-ASM, but the application will be developed in a reasonable amount of time and be reasonably easy to maintain.
