japronto VS json_benchmark

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.

If you're primarily targeting Python as an application layer, you may also want to check out my msgspec library[1]. All the perf benefits of e.g. yyjson, but with schema validation like pydantic. It regularly benchmarks[2] as the fastest JSON library for Python. Much of the overhead of decoding JSON -> Python comes from the python layer, and msgspec employs every trick I know to minimize that overhead.

[1]: https://github.com/jcrist/msgspec

[2]: https://github.com/TkTech/json_benchmark

- Adding nvme drive support to SMARTie, https://github.com/tktech/smartie, which is a pure-python cross-platform library for getting disk information like serial number, SMART attributes (like disk temperature) - json_benchmark, https://github.com/tktech/json_benchmark, which is a new benchmark and correctness test for the more modern Python JSON libraries - py_yyjson, https://github.com/tktech/py_yyjson, which is still a WIP and provides Python bindings to the yyjson library, which offers comparable speed to simdjson but more flexibility when parsing (comments, arbitrary sized numbers, Inf/Nan, etc) - And some fixes to https://github.com/TkTech/humanmark, which is a markdown library used to edit the README.md in json_benchmark above.