PyPy

I have been reading a lot about these tools recently and wondered if they are used in production, especially when building a backend with Python. As far as I know, PyPy is not ideal because of its lack of package support, but Cython seems pretty neat.

According to Wikipedia, both PyPy and Cython are chosen when speed is critical or a requirement in the matter.

Speed/perfomance is always a positive thing. But I guess we are sacrificing something else. But since the Python code is the same, we are still having its readibility, right?

Are we losing portability when using PyPy or Cython? Or something else like security?

Thank you in advance.

Standard Python with Cython has the benefit of C hooks and library support, but PyPy is pretty fast too. Doesn't PyPy get around the GIL and supports threading while Cython doesn't?

So what would you recommend for the best performance. Of course Cython will use cdef hooks whenever possible.

This year i started a journey to bring a really fast framework for Http, Http2, Http3 and WebSockets to Python and PyPy.

The problems with japronto and uvloop

Japronto don't get any github updates since 2020 and don't get any src update since 2018, japronto don't support SSL, WebSockets, , Windows or macOS Silicon. I really needed good support + performance, not only performance. Almost any solution out there have a really big performance hit when using PyPy3 or are not really fast enougth

The Solution

We discover a really fast, small, and well maintained C++ Library called , but no C API available, so we create and adapt the full C API from and will integrate libuv powered fetch and file IO, this same C API is used by

Benchmarking

We got about 770k req/s with the current version vs 582k from japronto using TechEmPower plaintext benchmarks and we still can get at least 30% or more performance once fully integrated with libuv + asyncio.

Socketify.py brings:

• WebSocket with pub/sub support

• Fast and realiable Http/Https

• Support for Windows, Linux and macOS Silicon & x64

• Support for and

This project aims to bring high performance PyPy3 web development and will bring:

• Fetch like API powered by libuv

• Async file IO powered by libuv

• Full asyncio integration with libuv

• Full Http3 support

• integration to better support , and

Install

For macOS x64 & Silicon, Linux x64, Windows

pip install git+https://github.com/cirospaciari/socketify.py.git
#or specify PyPy3
pypy3 -m pip install git+https://github.com/cirospaciari/socketify.py.git
#or in editable mode
pypy3 -m pip install -e git+https://github.com/cirospaciari/socketify.py.git@main#egg=socketify

Code Example

from socketify import App, AppOptions

app = App(AppOptions(key_file_name="./misc/key.pem", cert_file_name="./misc/cert.pem", passphrase="1234"))
app.get("/", lambda res, req: res.end("Hello World socketify from Python!"))
app.listen(3000, lambda config: print("Listening on port http://localhost:%d now\n" % config.port))
app.run()

Support

Socketify is an active project and will be maintained over time with security updates and new features and will be commercial support available

Check our github page:

All these different pythons but I don't understand what they are or what they do, for example Cpython's C-extensions, does that mean we can code C on a Python interpreter or something? Or like the resulting file will be able to be read by a C interpreter? As well a Jython, will Java be able to run its file? What's the resultant file... What's meant when people say Python is already compiled and can be run without the interpreter?

In socketify.py project i chose to support PyPy to get the performance close to Go Fiber, today i implemented one optimization, to reuse requests, responses and websockets objects to avoid dynamic allocations and GC pass. PyPy is just a beast, and i have a lot to optimize yet.

If you want to try run on your machine you can clone

Commands used to run on Debian GNU/Linux bookworm/sid x86_64 - kernel 6.0.0-2

sudo ./tfb --mode benchmark --test socketify.py --type plaintext 
sudo ./tfb --mode benchmark --test socketify.py-python3 --type plaintext 
sudo ./tfb --mode benchmark --test fiber --type plaintext

So I want to experiment and speed up my code. I have studied the basics of Cython and Numba but Pypy only has 10 year onld videos.

What I have found:

• Cython converts Python into C and makes the code useable in both Python and C

• Numba directly converts Python into Machine code and is useful for Math operations (numpy)

• Numba is JIT compiler

• Both Cython and Numba don't support 3rd party libraries like Pandas and spacy..

• Pypy is an implementation of Python. Normally the Python we use when we write python abc.exe in cmd is Cpython(not Cython).

• Numba and Cython speed up the code a lot if the code is compatible... things like list don't work with Numba...

Would be super helpful if someone can please explain the difference between Numba, Cython and Pypy and when to use which.

Even pointing me to the resources would be great!

Thanks in advance.

I'm curious to find out what people now think about these 3 tools. If I haven't used any of them, and I'm ready to dive into optimizing my code after profiling and identifying bottle necks.

Any suggestions would be appreciated! Thanks!