sanitizers VS pybind11

AddressSanitizer is really useful, it's similar to Valgrind but has much lower overhead.

And if you're up for it, I'd further recommend adding some ways to deal with buffer overflows in debug builds. The way I deal with this is by using Address-Sanitizer's manual poisoning api. Bonus point if you leave additional poisoned space between allocations so off by one errors are likely to end up in a poisoned region instead of nearby allocation.

It also doesn't hurt that Miri can find many kinds of unsafe violations even in unsafe blocks. Zig may get something like this one day, but even if it does, checking things at runtime is not a substitute for compile time -- the C++ Sanitizers haven't exactly solved the safety story for C++ even over a decade later.

This is where stuff like ASan is really useful.

Use: https://github.com/google/sanitizers/wiki/AddressSanitizer

Have you tried using tools like ASAN/LSAN or valgrind to confirm that there are indeed no memory leaks?

Compile with ASAN and UBSAn

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.