Thank you @Asd! Map is generally faster at mutations and lookups - potentially much faster as collection size grows. Trees are useful when it’s a requirement to have the data sorted at all times with scalable performance.
Regarding benchmarks, I did use Benchee for a long time but struggled to get stable performance curves covering a wide range of collection sizes (0 to 15k) for the same test (the graphs on the report), or getting consistent results across runs without very long warm up times (and even then).
I solved this with a custom benchmarking framework that maximizes CPU cache pressure while keeping testing conditions overall uniform for all the cases, and that runs repeatedly until some semblance of stability has been achieved instead of running for a fixed amount of time. To be honest, a lot of the work in this project went into benchmarking alone.
