Got myself a few months ago into the optimization rabbit hole as I had a slow quant finance library to take care of, and for now my most successful optimizations are using local memory allocators (see my C++ post, I also played with mimalloc which helped but custom local memory allocators are even better) and rethinking class...
I’ve got so many more stories about bad optimizations. I guess I’ll pick one of those.
There was an infamous (and critical) internal application somewhere I used to work. It took in a ton of data, putting it in the database, and then running a ton of updates to populate various fields and states. It was something like,
Put all data in x table with batch y.
Update rows in batch y with condition a, set as type a. (just using letters as placeholders for real states)
Update rows in batch y that haven’t been updated and have condition b, set as type b.
Update rows in batch y that haven’t been updated and have condition c, set as type c.
Update rows in batch y that have condition b and c and condition d, set as type d.
(Repeat many, many times)
It was an unreadable mess. Trying to debug it was awful. Business rules encoded as a chain of sql updates are incredibly hard to reason about. Like, how did this row end up with that data??
Me and a coworker eventually inherited the mess. Once we deciphered exactly what the rules were and realized they weren’t actually that complicated, we changed the architecture to:
Pull data row by row (instead of immediately into a database)
Hydrate the data into a model
Set up and work with the model based on the business rules we painstakingly reverse engineered (i.e. this row is type b because conditions x,y,z)
Insert models to database in batches
I don’t remember the exact performance impact, but it wasn’t markedly faster or slower than the previous “fast” SQL-based approach. We found and fixed numerous bugs, and when new issues came up, issues could be fixed in hours rather than days/weeks.
A few words of caution: Don’t assume that building things with a certain tech or architecture will absolutely be “too slow”. Always favor building things in a way that can be understood. Jumping to the wrong tool “because it’s fast” is a terrible idea.
In another project, I had some throwaway code, where I used a naive approach that was easy to understand/validate. I assumed I would need to replace it once we made sure it was right because it would be too slow.
Turns out it wasn’t a bottleneck at all. It was my first time using Java streams with relatively large volumes of data (~10k items) and it turned out they were damn fast in this case. I probably could have optimized it to be faster, but for their simplicity and speed, I ended up using them everywhere in that project.
Do some tasks run slower today than they did in the past? Sure. Are there some that run slower without a good reason? Sure.
But the whole article just kind of complains. It never acknowledges that many things are better than they used to be. It also just glosses over the complexities and tradeoffs people have to make in the real world.
Like this:
Windows 10 takes 30 minutes to update. What could it possibly be doing for that long? That much time is enough to fully format my SSD drive, download a fresh build and install it like 5 times in a row.
I don’t know what exactly is involved in Windows updates, but it’s likely 1) a lot of data unpacking, 2) a lot of file patching, and 3) done in a way that hopefully won’t bork your system if something goes wrong.
Sure, reinstalling is probably faster, but it’s also simpler. If your doctor told you, “The cancer is likely curable. Here’s the best regimen to get you there over the next year”, it would be insane to say, “A YEAR!? I COULD MAKE A WHOLE NEW HUMAN IN A YEAR!” But I feel like the article is doing exactly that, over and over.
Large companies don’t innovate anymore, they acquire and Google’s sourceforge offering is… let’s call it underwhelming. If they tried to compete with Github and threw money into Gitlab, what do you think they’d do?
This level is when 80s kids learned for the first time that it's ok to give up. (lemmy.zip)
Which language you wish would really grow and reach mainstream adoption?
Assume mainstream adoption as used by around 7% of all github projects...
What are some of the best optimizations you applied to your code?
Got myself a few months ago into the optimization rabbit hole as I had a slow quant finance library to take care of, and for now my most successful optimizations are using local memory allocators (see my C++ post, I also played with mimalloc which helped but custom local memory allocators are even better) and rethinking class...
Software disenchantment (tonsky.me)
Would the world be a better place if Google bought Gitlab?
Large companies don’t innovate anymore, they acquire and Google’s sourceforge offering is… let’s call it underwhelming. If they tried to compete with Github and threw money into Gitlab, what do you think they’d do?
Question about gdscript: Which is more efficient?
I’m just curious about which is the most efficient way of doing this kind of node enumiration:...