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:
- Python, Java etc. execute their compiler/interpreter while the program is running.
- CLIs are magnitudes slower, because these languages require a runtime to be launched before executing the CLI logic.
- GUIs and simulations stutter around, because these languages use garbage collection for memory management.
- And then just death by a thousand paper cuts. 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.
And when working with multiple threads in Java, it is considered best practice to always clone memory of basically anything you touch. Like, that’s good code and its performance will be mediocre. Also, you better don’t think about using multiple threads in Python+JS. For those two, even parallelism was an afterthought.
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.
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.
There are a few reasons for this, some of the most important being:
- The languages were not designed with speed primarily in mind and as such made some design decisions that fundamentally cannot be optimized around
- Authors of programs in these languages prioritize things other than performance when writing the programs.
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.
Don’t throw the baby out with the bath water. There are many applications that don’t have performance needs such as a calculator app
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.
