poltaffiliates.blogg.se

#Psycopg3 install#
#Psycopg3 update#
#Psycopg3 driver#

I'm excited for HPy as that would help solve some of the packaging issues we ran into. A single one of these would not have been a show-stopper, but it's more the aggregate of them.

If we pip-tools compile on CPython 3.7, it would erase the dataclasses python_version < '3.7' requirement for when PyP圓.6 installed.Īgain, I'm writing this in the hopes that it helps shed light on the kinds of papercuts that can arise.

Less of an issue, but just generally wanting to use newer Python 3.7/3.8 language features such as _future_ annotations and dataclasses.

#Psycopg3 update#

We couldn't determine if this was due to a Django/pytest/pytest-django update, a P圜harm update, or something else but we got frozendict/ update exceptions any time we tried to debug our unit tests.

A strange (never could figure this issue out) issue arose recently where we could no longer debug pytest/Django unit tests via P圜harm using the PyPy interpreter.PyPy Docker image sometimes out of date.We have developers on macOS, Linux, and Windows. This may be a bit too much of a challenge if you have no idea to begin. PyPy is not compatible with mypy, meaning we had to have a separate env that only ran under CPython to run mypy.

#Psycopg3 install#

We also never got PyPy working on Windows for our project because of this and other sdist related issues. If prerequisites are met, you can install psycopg like any other Python package, using pip to download it from PyPI: pip install psycopg2.

Installing from sdist added extra complications of needing system libraries installed on Linux and macOS.

pyscogp2cffi uses setup_requires, which means it pulls packages in a way that cannot be controlled via pip configuration.

It wasn't clear if we could use psycopg-binary and if there would be a performance loss. We use PostgreSQL and this was worrying to our team for a long term maintenance. The maintenance papercuts that added up are:

#Psycopg3 driver#

Our project is a Django/DRF application where we saw JSON serialization speed ups of 2-3x using PyP圓 for deeply nested JSON serialization. discusses Open Source licensing, humble drivers, photography, and the nature of adapt or die with Daniele Varrazzo, Creator of Python Driver Psycopg3. Psycopg 3 presents a familiar interface for everyone who has used Psycopg 2 or any other DB-API 2.0 database adapter, but allows to use more modern PostgreSQL and Python features. From the article here it seems that its behaviour will be similar to asyncpg wrt execution. Psycopg 3 is a newly designed PostgreSQL database adapter for the Python programming language. Again, thank you for working on this project.įor us, ultimately it came down to too many small maintenance paper cuts to justify the extra speed we received from PyPy. SQLAlchemy will need to add support for psycopg3 at some point. If you are interested in keeping track of when the library will be. I just wanted to leave a message about why my team has decided to stop using/supporting PyP圓 at this time, in hopes that the feedback is useful for PyPy developers. Even though you used psycopg2 in this recipe, keep in mind that psycopg3 is in the works. I think it shows how valuable a JIT compiler can be for Python code, and I hope it continues to grow. I first want to say I'm very grateful for the PyPy project.