uvloop VS Numba

I wound up on a different team with pre-existing Python code so temporarily shelved my use of Go for a bit, and we used Sanic (an async Python framework built on top of the excellent uvloop & libuv that also powers Node.js) to build some APIs for live channel management & operations. We hand-wrote our OpenAPI and used it to generate documentation and a CLI, which was an improvement over what was there (or not) before. Other teams used the OpenAPI document to generate SDKs to interact with our service.

If you're looking for maximum requests per second you can change the asyncio event loop with one like UVLoop.

An optional package uvloop can also be install if working on Linux:

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 are building server-side applications using Python 3 and async API and if you didn't use https://github.com/MagicStack/uvloop, you are missing out on performance big time.

Also, if you happen to build microservices, don't forget to try PyPy, that's another easy performance booster (if it's compatible to your app).

Not that bad actually, with a different event loop implementation (such as https://github.com/MagicStack/uvloop). Not sure how well it will perform in a browser though

And so I did some searching, and found that SearX isn't officially supported on Windows. Not to be deterred, I did another quick search and found that with pip and/or docker, you should be able to install SearX straightforwardly on Windows. After trying this for a bit, I realized that uvloop, a (questionably optional dependency of SearX) is not supported on Windows. I tried a couple things to get it to work, but they didn't end up working for me either through user error, ignorance, or plain old not working.

they also wrote uvloop [0] which is fantastic and advances the cutting edge of what can be done with modern asyncio-based Python. I saw a ~3x improvement in the throughput of a microservice I wrote when I first tried it out years ago. currently at $dayjob we just use it by default in every Python service, whether or not we expect that service to be performance-critical.

0: https://github.com/MagicStack/uvloop

If you manage to grok how uvloop works as well as Python's default asyncio loop scheduler, you'll understand this style. It is not by itself a parallelism enabler, but network I/O the coroutines triggered would run in parallel nevertheless, though CPU bound computations would not by default.

Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code.

Simulations are, at least in my experience, numba’s [0] wheelhouse.

[0]: https://numba.pydata.org/

That's very cool. Numba introduces just-in-time compilation to Python via decorators and its sole reason for being is to turn everything it can into abstract syntax trees.

A super-fast backtesting engine built in NumPy and accelerated with Numba.

Regarding speed, I don't agree this is a good argument against Python. For example, it seems no one here has yet mentioned numba, a Python JIT compiler. With a simple decorator you can compile a function to machine code with speeds on par with C. Numba also allows you to easily write cuda kernels for GPU computation. I've never had to drop down to writing C or C++ to write fast and performant Python code that does computationally demanding tasks thanks to numba.

Just for reference,

* Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11."

* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.

* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."

* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."

* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"

[0] https://github.com/Nuitka/Nuitka

[1] https://www.pypy.org/

[2] https://cython.org/

[3] https://numba.pydata.org/

[4] https://github.com/pyston/pyston

For the benefit of future readers: https://numba.pydata.org/

Taichi (similar to numba) is a python library that allows you to write high speed code within python. So your program consists of slow python that gets interpreted regularly, and fast python (fully type annotated and restricted to a subset of the language) that gets parallellized and jitted for CPU or GPU. And you can mix the two within the same source file.