JavaCPP VS Cython

JavaCPP and presets for working with JNI

In the meantime you might want to check out JavaCPP: https://github.com/bytedeco/javacpp

Maybe you can use JavaCPP?

Here is the code for JNI, which uses the prebuilt JavaCPP library to call the getpid function. We don't have to write all the manual C binding code and rituals as the JavaCPP library already does it.

Javacpp is the best ffi library of all https://github.com/bytedeco/javacpp

As for our approach, we maintain a library called javacpp: https://github.com/bytedeco/javacpp which proves a python wheel like experience where we distribute natively optimized c/c++ code (and even cuda accelerated code) as jar files on maven central. We also are able to develop with a python like experience by passing pointers around and other low level constructs directly allowing optimizations that you typically only get in c/c++.

https://github.com/bytedeco/javacpp

* it maps naturally and efficiently many common features afforded by the C++ language and often considered problematic, including overloaded operators, class and function templates, callbacks through function pointers, function objects (aka functors), virtual functions and member function pointers, nested struct definitions, variable length arguments, nested namespaces, large data structures containing arbitrary cycles, virtual and multiple inheritance, passing/returning by value/reference/string/vector, anonymous unions, bit fields, exceptions, destructors and shared or unique pointers (via either try-with-resources or garbage collection), and documentation comments*

and again, everything that was released after using an older version of cython.

Packages using cython (note the linked issues) due to this change.

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

Profile and optimize the hotspots with cython (or whatever the cool kids are using these days... It's been a while.)

Cython - - embed demo

There are also just-in-time compilers available for some Python features, that compile those parts to machine code. That includes Numba (usable as a library within CPython) and Pypy (an alternative Python implementation that includes a JIT compiler to improve performance). There’s also Cython, which is a superset of Python that allows more directly interfacing with C and C++ functions, and compiling the resulting combined code.

Conceptually, Cython is mainly for accelerating Python code, and can _also_ access C code. Meanwhile CFFI is specifically for calling C code and nothing else. I recommend the video for the differences.

One concrete thing that pops to my mind is that Cython doesn't support Py_LIMITED_API which means that you need to ship a lot more binary wheels. At least the issue is still open (https://github.com/cython/cython/issues/2542) and Cython projects IME need new wheels for each minor Python release. Compare that to cffi projects that (musl & pypy aside) only have to ship wheels for one Python version / architecture: https://pypi.org/project/argon2-cffi-bindings/#files

As you can see, in this benchmark Starlite handily beats even blakchseep, a notoriously fast ASGI framework written in Cython

How does this relate to

https://cython.org/

?

Would it be possible to write performance-sensitive parts of a Python system in Codon and link that to a CPython or PyPy runtime that supports more dynamic features?