setuptools VS GORM

To be fair, that seems to have been a 2 year warning:

https://github.com/pypa/setuptools/commit/3544de73b3662a27fa...

There was/is some discussion in setuptools about how to normalize the tarball (https://github.com/pypa/setuptools/issues/2133#issuecomment-...) coudl something similar be applied to Building Python itself ?

❯ yay -Sy python-setuptools python-jaraco.text ❯ pip show setuptools Name: setuptools Version: 67.7.0 Summary: Easily download, build, install, upgrade, and uninstall Python packages Home-page: https://github.com/pypa/setuptools Author: Python Packaging Authority Author-email: [email protected] License: Location: /usr/lib/python3.11/site-packages Requires: jaraco.text, more-itertools, ordered-set, packaging, platformdirs, tomli, validate-pyproject Required-by: Cerberus, fs, httpie, input-remapper, pecan, pycountry, python-lsp-server, reuse, setuptools-scm, zc.lockfile

Link: https://github.com/pypa/setuptools/issues/3772

For example, when it comes to Python, one option is to use the same packaging system that a huge number of open-source libraries and tools are published with. You can use setuptools or Hatch to build a "packaged" version of your code, and publish it to either the public PyPi repository or an internal one that you set up. Then your users can use pip to install your package, automatically fetch its dependencies, and keep it up to date, just like any other Python module.

You could maybe make it a click Application, and use setuptools.

Not sure how advisable it is to depend on setup.py given the setuptools team has very clearly stated that they are not interested in supporting any cli commands anymore including setup.py install. Relavant PR

GORM is a comprehensive ORM tool in Go, offering a code-first approach which allows defining database schemas using struct tags in Go. It's known for its developer-friendly nature, making it suitable for both beginners and experienced users. GORM supports a variety of SQL databases like MySQL, PostgreSQL, and SQLite. It's designed to be flexible, allowing developers to drop down to raw SQL when necessary. However, it's important to be cautious about its performance implications in large-scale applications.

Homepage: https://gorm.io/

Since most of these APIs will be simple CRUD (Create/Read/Update/Delete) endpoints, let's build this service using GORM, an ORM library that makes building CRUD endpoints really simple.

After successfully configuring the application, it's time to delve into integrating the data layer. For this purpose, I will utilize gorm, a powerful SQL ORM that facilitates rapid development of the data layer using model structs.

For basic INSERT ... VALUES ... or SELECT ... WHERE ... JOIN ..., use a library such as GORM. For INSERT .... SELECT ... statements where one combines two round trips (SELECT and INSERT) into one, ORMs have a hard time performing this query. Particularly when you start doing joins. Joins are the heart of Relational database theory (they denote relationships). So get to know what an INNER, LEFT, RIGHT and OUTER join is and why you would use them. Also learn INSERT ... SELECT ...

I have never hated gorm and it serves me well. However I tend to feed it raw SQL very often.

Array of values with embedded value all pointed to the last value, reflect code was broken: https://github.com/go-gorm/gorm/pull/5901 data corruption