psycopg2 VS LocalStack

You can practice both by using https://www.psycopg.org from your Python code to communicate with your database. When I wanted to practice some SQL, that's what I did (we use psycopg at work, so that's what I practiced with, making a dream journal thingy for myself that was better than just noting stuff in a notepad because I could then look up e.g. what other stuff was correlated with Y, how many times I dreamed of X, etc. etc.)

But seems like it should work with Django 3, which you have specified https://github.com/psycopg/psycopg2/issues/1293

If you're using Postgres for your SQL, look at the "copy' method of the psycopg module (see https://www.psycopg.org/articles/2020/11/15/psycopg3-copy/) . It's much faster than INSERTs in my experience (YMMV).

Back when I used Psycopg2, there was no -binary package, so you'd get libpq set up similarly to pg-native. Docs say:

> The binary package is a practical choice for development and testing but in production it is advised to use the package built from sources.

Relevant GitHub discussion: https://github.com/psycopg/psycopg2/issues/674

I dunno, this seems worse to me.

Just go to the more easily readable docs here. I’m sorry, but the linked article is terrible.

psycopg2-binary. See https://github.com/psycopg/psycopg2/issues/1286.

psycopg2-binary, PostgreSQL Database adapter for python.

... # psycopg2==2.9.3 # https://github.com/psycopg/psycopg2 ...

I sometimes do this in testing. For example, consider the library used to communicate with a Postgres database, psycopg.

Later you can use any S3 compatible storage because the code I write will still work, but for testing purposes on my local machine, I will use LocalStack:

Acesse o site da LocalStack e faça login.

To give this a slightly different spin:

--> "The best optimization is simply not spinning things up."

At least for local development and testing, as made possible by LocalStack (https://localstack.cloud), among other local testing solutions and emulators.

We've seen so many teams fall into the trap of "someone forgot to shut down dev resource X for a week and now we've racked up a $$$ bill on AWS".

What is everyone's strategy to avoid this kind of situation? Tools like `aws-nuke` (https://github.com/rebuy-de/aws-nuke) are awesome (!) to clean up unused resources, but frankly they should not be necessary in the first place.

(For others who hadn't heard of it: localstack is

LocalStack Website

O Localstack: https://localstack.cloud/, é um recurso que possibilita simular diversos recursos AWS (dynamoDB, s3, iam, cognito, ses), dentro da sua máquina, utilizando o docker.