japronto VS json-buffet

100x faster than FastAPI seems easy. I wonder how it compares to other fast Python libraries like Japronto[1] and non-Python ones too.

1 - https://github.com/squeaky-pl/japronto

The source code from the project resides in the github, with more than 8.6k stars and 596 forks is a very popular github, but no new releases are made since 2018, looks pure much not maintained anymore, no PR's are accepted no Issues are closed, still without windows or macOS Silicon, or PyPy3 support. Japronto it self uses uvloop with more than 9k stars and 521 forks and different from japronto is seems to be well maintained.

And here's the code: https://github.com/multiversal-ventures/json-buffet

The API isn't the best. I'd have preferred an iterator based solution as opposed to this callback based one. But we worked with what rapidjson gave us for the proof of concept.

Ha! Thanks to you, Today I found out how big those uncompressed JSON files really are (the data wasn't accessible to me, so i shared the tool with my colleague and he was the one who ran the queries on his laptop): https://www.dolthub.com/blog/2022-09-02-a-trillion-prices/ .

And yep, it was more or less they way you did with ijson. I found ijson just a day after I finished the prototype. Rapidjson would probably be faster. Especially after enabling SIMD. But the indexing was a one time thing.

We have open sourced the codebase. Here's the link: https://github.com/multiversal-ventures/json-buffet . Since this was a quick and dirty prototype, comments were sparse. I have updated the Readme, and added a sample json-fetcher. Hope this is more useful for you.

Another unwritten TODO was to nudge the data providers towards a more streaming friendly compression formats - and then just create an index to fetch the data directly from their compressed archives. That would have saved everyone a LOT of $$$.