Too many abstraction layers
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.
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.
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.
What if the language has a bad design?
Then use it poorly and it’s a double negative.
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’ve been vibecoding pyqt based python apps lately. The performance even one of the so called “slowest” languages can do is more than fine. Not using Electron, or actually optimizing matter more than the language itself, most of the time anyway.
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.
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.
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.”
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.
