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 layouts in a more “data-oriented” way (mostly going from array-of-structs to struct-of-arrays layouts whenever it’s more advantageous to do so, see for example this talk).
What are some of your preferred optimizations that yielded sizeable gains in speed and/or memory usage? I realize that many optimizations aren’t necessarily specific to any given language so I’m asking in !programming@programming.dev.
A database optimization I made was changing a tables id generation from a manual generation scheme (some other table had an entry with the next usable id, it was updated with every entry written) to a uuid generation scheme. The table stores data from a daily import, on a fresh import all previous data is deleted. On some systems, there are more than 10 000 000 entries to be imported on a daily basis, which took 8 hours. Now, with batched inserts and the mentioned improvement in the db scheme, it’s at about 20 minutes.
TLDR: Reducing the amount of queries sent is good, because although network is usually fast (ms), db requests are still slow compared to the speed of an application (clock cycles).
And yeah, there is an option to only import changes daily, but sadly that isn’t supported in every environment.