cannoli VS polars

This is a great read, and it's fantastic to see all the work being done to evaluate and improve the language!

Shameless plug alert: the dynamic-nature of the language is actually something that I had studied a few years back [1]. Particularly the variable and object attribute look ups! My work was just a master's thesis, so we didn't go too deep into more tricky dynamic aspects of the language (e.g. eval, which we restricted entirely). But we did see performance improvements by restricting the language in certain ways that aid in static analysis, which allowed for more performant runtime code. But for those interested, the abstract of my thesis [2] gives more insight into what we were evaluating.

Our results showed that restricting dynamic code (code that is constructed at run time from other source code) and dynamic objects (mutation of the structure of classes and objects at run time) significantly improved the performance of our benchmarks.

[1]: https://github.com/joncatanio/cannoli

[2]: https://digitalcommons.calpoly.edu/theses/1886/

This is because 0.1 is in actuality the floating point value value 0.1000000000000000055511151231257827021181583404541015625, and thus 1 divided by it is ever so slightly smaller than 10. Nevertheless, fpround(1 / fpround(1 / 10)) = 10 exactly.

I found out about this recently because in Polars I defined a // b for floats to be (a / b).floor(), which does return 10 for this computation. Since Python's correctly-rounded division is rather expensive, I chose to stick to this (more context: https://github.com/pola-rs/polars/issues/14596#issuecomment-...).

https://github.com/pola-rs/polars/releases/tag/py-0.19.0

That turned out to be related to pola-rs/polars#11912, and this linked comment provided a deceptively simple solution - use PARSE_DECLTYPES when creating the connection: