spdlog VS pybind11

Why a new lib instead of using or contributing to an existing one as spdlog?

https://github.com/gabime/spdlog

GitHub repo: gabime/spdlog

SpdLog https://github.com/gabime/spdlog

The usual recommendation when logging libraries are brought up is spdlog, which is however header-only. It's available on Conan-center.

* Aha! In digging up the docs for NDC, I found this[1], which does mention a book for your reading list: "Patterns for Logging Diagnostic Messages" part of the book "Pattern Languages of Program Design 3" edited by Martin et al.

[1] https://github.com/gabime/spdlog

FetchContent_Declare( spdlog GIT_REPOSITORY https://github.com/gabime/spdlog GIT_TAG origin/v1.x ) FetchContent_MakeAvailable(spdlog)

Typically, you'd use a logging library to handle stuff like this. I personally like spdlog. You use different logger functions (info, warn, error) and depending on what level you have set for the logger (or globally) some of the functions become no-ops. E.g. When not running in verbose mode all spdlog::info() do nothing.

I'm investigating using C++ to build a REST server, and would love to know of people's experiences with Crow-- or whether they would recommend something else as a "medium-level" abstraction C++ web server. As background, I started off experimenting with Python/FastAPI, which is great, but there is too much friction to translate from pybind11-exported C++ objects to the format that FastAPI expects, and, of course, there are inherent performance limitations using Python, which could impact scaling up if the project were to be successful.

I have a C++ function computing a large tensor which I would like to return to Python as a NumPy array via pybind11.

This is based on the Google Ink Stroke Modeler C++ library, and using pybind11 to make it available on python.

pybind11 makes it easy to call C++ from Python if you want to mix.

If you've never used Pybind before these pybind tests[1] and this repo[2] have good examples you can crib to get started (in addition to the docs). Once you handle passing/returning/creating the main data types (list, tuple, dict, set, numpy array) the first time, then it's mostly smooth sailing.

Pybind offers a lot of functionality, but core "good parts" I've found useful are (a) use a numpy array in Python and pass it to a C++ method to work on, (b) pass your python data structure to pybind and then do work on it in C++ (some copy overhead), and (c) Make a class/struct in C++ and expose it to Python (so no copying overhead and you can create nice cache-aware structs, etc.).

[1] https://github.com/pybind/pybind11/blob/master/tests/test_py...

I have a python module for which the logic is written in C++ and I use pybind11 to expose the objects and functions to Python.

Reading from Python requires a wrapper, using pybind11 this is fairly done.

Also I can highly recommend starting using C++ to augment your Python code, i.e. find the parts that are slow or undoable in Python and write those in C++ then expose them as Python functions. You can use https://github.com/pybind/pybind11 to call C++ code from Python.