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Honestly, it's still ridiculous to me how slow Python, Java, JS, Ruby etc. continue to feel, even after decades of hardware optimizations. You'd think their slowness would stop being relevant at some point, because processors and whatnot have become magnitudes faster, but you can still feel it quite well, when something was implemented in one of those.
Many of these have C-bindings for their libraries, which means that slowness is caused by bad code (such as making a for loop with a C-call for each iteration instead of once for the whole loop).
I am no coder, but it is my experience that bad code can be slow regardless of language used.
Bad code can certainly be part of it. The average skill level of those coding C/C++/Rust tends to be higher. And modern programs typically use hundreds of libraries, so even if your own code is immaculate, not all of your dependencies will be.
But there's other reasons, too:
Well, and then all of the above feeds back into all the libraries not being performant. There's no chance to use the languages for performance-critical stuff, so no one bothers optimizing the libraries.
Java is still significantly faster and more efficient than Python tho - because it has ahead-of-time optimizations and is not executing plain text.
Idk numpy go brrrrrrrrrr. I think it's more just the right tool for the right job. Most languages have areas they excel at, and areas where they're weaker, siloing yourself into one and thinking it's faster for every implementation seems short sighted.
At it's heart, numpy is C tho. That's exactly what I'm talking about. Python is amazing glue code. It makes this fast code more useful by wrapping it in simple(r) scripts and classes.
Faster, sure. Efficient, fuck no. With Java you have to run around and write ton of boiler plate code to do something simplest in nature.
Could say the same for C/C++.
But yeah I'd like it if the features given by Lombok were standard in the language though it's not a big deal these days since adding Lombok support is very trivial.
You shouldn’t use Lombok, as it uses non-public internal Java APIs, which is why it breaks every release. At one point we had a bug with Lombok that only resolved if you restarted the application. Switching off of Lombok resolved the issue.
Just switch to kotlin. You can even just use Kotlin as a library if you really want (just for POJOs), but at this point Kotlin is just better than Java in almost every way.
I agree but I have tried like hell to get my team to use Kotlin but it's hard to convince upper management. The team is reluctant to switch as well.
Using Lombok is the next best thing.
Though for POJOs that are immutable you can use record classes now.
I'm mainly talking efficiency in terms of energy use. I won't deny that some ugly decisions have been made with Java :D
For example, when iterating over text, you can't tell it to just give you a view/pointer into the existing memory of the text. Instead, it copies each snippet of text you want to process into new memory.
As someone used to embedded programming, this sounds horrific.
Yep. I used to code a lot in JVM languages, then started learning Rust. My initial reaction was "Why the hell does Rust have two string types?".
Then I learned that it's for representing actual memory vs. view and what that meant. Since then I'm thinking "Why the hell do JVM languages not have two string types?".
At least with Java, its the over(ab)use of Reflections and stuff like dependency injection that slows things down to a crawl.
There are a few reasons for this, some of the most important being:
Speed is not just about processors becoming faster - this is a large part of why DSA is important to learn as a programmer.
That's because it's not relevant. Speed can be compensated for either by caching or outsourcing your load, if there's such a huge need to process large amount of data quickly. In day to day work I can't say I have ever ran into issues because code was executing slow. Normal operation Python is more than capable of keeping up.
On the other side of the coin you have memory management, buff and stack overflows and general issues almost exclusive to C, which is something you don't have to worry about as much with higher level languages. Development with Python is simply faster and safer. We as developers have different tools, and we should use them for their appropriate purpose. You can drive nails with a rock as well, but you generally don't see carpenters doing this all day.
You can sometimes deal with performance issues by caching, if you want to trade one hard problem for another (cache invalidation). There's plenty of cases where that's not a solution though. I recently had a 1ns time budget on a change. That kind of optimization is fun/impossible to do in Python and straightforward to accomplish Rust or C/C++ once you've set up your measurements.
True, plus the bloated websites I see are using hundreds of thousands of lines of JavaScript. Why would you possibly need that much code? My full fledged web games use under 10,000.
"Slow"
They aren't as fast as a native language but they aren't all that slow if you aren't trying to use them for performance sensitive applications. Modern machines run all those very quickly as CPUs are crazy fast.
Also it seems weird to put Java/OpenJDK in the list as it is in its own category from my experience
Java is certainly the fastest of the bunch, but I still find it rather noticeable how long the startup of applications takes and how it always feels a bit laggy when used for graphical stuff.
Certainly possible to ignore that on a rational level, but that's why I'm talking about how it feels.
I'm guessing, this has to do with just the basic UX principle of giving the user feedback. If I click a button, I want feedback that my click was accepted and when the triggered action completed. The sooner those happen, the more confident I feel about my input and the better everything feels.
I've never experienced that. Also Android is OpenJDK based and the applications in Android work well and the system is well optimized
It is always a question of chosing the right tool for the right task. My core code is in C (but probably better structured than most C++ programs), and it needs to be this way. But I also do a lot of stuff in PERL. When I have to generate a source code or smart-edit a file, it is faster and easier to do this in PERL, especially if the execution time is so short that one would not notice a difference anyway.
Or the code that generates files for the production: Yes, a single run may take a minute (in the background), but it produces the files necessary for the production of goods of over 100k worth. And the run is still faster than the surrounding processes like getting the request from production, calculating the necessary parameters, then wrapping all the necessary files with the results of the run into a reply to the production department.
Damn, that's a fast ass-cheetah!
What with passenger planes like the one depicted typically going 575-600 mph and the bar only 3 times as long, that dude is sprinting at upto 200mph!
(Link is to classic xkcd comic, not furry porn)
Link is to classic xkcd comic, not furry porn
Damn, you got my hopes up for a second there.
(Link is to classic xkcd comic, not furry porn)
I am both disappointed and pleased.
Well if it's linear the cheeta is going somewhere around 100 million miles per hour. Air resistance would quickly make it a no-ass cheeta.
Log scaling?
Probably, but it's much more fun to imagine a cheetah hauling ass at the speed of a modern supercar 😂
That's just the FreePascal Compiler
As someone who only codes solutions for himself I don't relate. All the extra time I would spend writting a C solution it would never attone the runtime loses of doing it in maybe python.
Anyway, this is only relevant if you're writing only for yourself. It's your poor users who suffer for your expediency.
I used to write extensively with C++, but it has been a long time since speed mattered that much to one of my applications. I still marvel at the cache-level optimizations some people come up with, but I'm in the same mindset as you now.
My workload split of Data Movement vs Data Transformation is like 95:5 these days, which means almost all the optimizations I do are changing batch/cache/page/filter settings. I can do that in any language with http bindings, so I choose those that are faster to write.
I used c++ in college, and I think it's useful to know c because so much relies on it. That said if I'm going to do something that needs performance I'll look to go first, then rust if go isn't a good fit, but that's mostly because I know go better. Both are excellent languages.
If I just need something functional quick and easily I'll turn to Python. If I need a net service quick node.js is great.
I use c++ whenever possible because I like classes and objects and having more versatility to make more dynamic programs. I made an entire kernel that way one time because fuck the police.
Haha I love it. c++ is definitely super useful. I never got that deep with it but I've certainly benefited from many things written in c++. Wrote small things and I've had to debug it on occasion just to get something working. It usually ended up being a compiler flag I had to set. I ended up going into web and network related stuff after college. Perl was my goto back then but I'm loving these newer languages and the thought put into some of it. For example the struct, interfaces, and type systems in go could probably replicate a lot of what you would use the classes and objects for.
I was a huge C++ fan back when I was doing a bunch of competitive programming. If I need a performant project nowadays, I look to golang first. It gives me the speed of a compiled language with the usability of high-level language. I still solve the occasional Advent of Code in C++, though :)
https://github.com/hykilpikonna/hyfetch
I like hyfetch because it has the largest os compatibility.
It feels like the spiritual successor to neofetch tbh.
yeah and it allows for queering up the distro logo, which I absolutely love.
It's still slow and written in Bash/Python
Honestly, love to see a lil CatchyOS representation. So far my favorite Arch flavor.
It's fitting for this image too since CachyOS kernel + packages + defaults go zoom.
I decided to run it on system without bash as neofetch replacement. It feels unreasonably complex, I spent unreasonable amount of time fighting with cmake. On normal system I have pfetch in my bashrc, since it's basically instant, and neofetch for screenshots. It's not instant, but I don't run it every second or something.
And btw I do love speed and simplicity of C, but for fetch tool, POSIX shell is the best choice, I think.