I've plugged this several times here (just a fan) but chdb's DataStore is a "lazy" drop-in replacement for pandas dataframe. Pandas API with chdb performance. I don't use it myself (I do everything mostly in sql with either duckdb or chdb) but always found pandas more convinient than polars for quick and dirty data crunching (less typing and frankly more pythonic if you're used to slicing).
My comment is to say the US has proven how brutal they are consistently through all the wars of aggression they have waged in the past several decades. They do not see their "enemies" as human. I can't fix anything unfortunately.
It's incredible, after all the grotesque stories about rape, torture and murder of children, men and women during the Iraq war, active support of genocide (and 10s of thousands of children murdered by Israel, on purpose), prisoners rape and child imprisonment, a "secretary of war" and president publicly admiting to war crimes and saying things like "negotiate with bombs" you still "refuse to believe" that anyone in decision chain wouldn't do anything like this.
chdb's new DataStore API looks really neat (drop in pandas replacement) and exactly how I envisioned a faster pandas could be without sacrificing its ergonomics
I think that's a sane take. Indeed, I think most data analysts find it much easier to use pandas over polars when playing with data (mainly the bracket syntax is faster and mostly sensible)
I just ordered this monitor earlier this week (still waiting for it to be delivered), did you need to tweak it or use some thing like MacType like others are suggesting in this thread?
Indeed, I was delighted when I read the part about wutang's time capsule and obviously OP is a wu-tang and general hip hop fan. The intro you shared is dope!
She is wonderful and her work on investigating the complicity of countries in the Gaza genocide and Palestinian rights / Israeli agression is incredibly thorough and extremely important. More power to her!
That doesn't cover OP's point, some people's brains just work differently and they can achieve something in 1000x less time than others. You can have all the time in the world and you'll never reach their level. That's essentially what talent is.
As already mentioned in other comments, it doesn't really make sense to compare to json parsers since lite3 parses, well, lite3 and not json. It serves a different use case and I think focusing on performance vs json (especially json parsers) is not the best thing about this project
I've recently updated an internal tool which basically acts as a configuration and dependency/context manager for performing hundreds of api calls. I added an httpx backend (to test vs the current urllib3 backend) and also introduced an async API (httpx as well). However, from your benchmarks it seems like I should've went with aiohttp for faster async? I will work on integrating pyreqwest as well