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.
Staying on the SQL theme… The company I work for has a fairly old (~20 years) system. There’s a feature for users and site admins to export massive amounts of data, with the option to export data from when the system was first released. Purely CSV or XML data formats. On large datasets, the time for export would vary from 10-20+ hours, and would frequently timeout, forcing you to split exports into multiple timeframes and manually merging them into a single file. The solution? Indexes! Indexes were non-existent. After adding them, export times have dropped to ~10-15 minutes, which is a rather insane performance increase, especially since a single export is accepted per account at a time.