asgiref VS SQLAlchemy

uvicorn is an ASGI server that is recommended for running FastAPI applications.

I can't seem to be able to edit on mobile. OP either meant this, or its variation, such as async_to_sync and sync_to_async.

https://github.com/django/asgiref/blob/main/asgiref/sync.py

Ofc this is a python example. I have no idea how it works in different languages.

FastAPI is a popular Python Web framework that developers use to create RESTful APIs. It is based on Pydantic and Python-type hints that assist in the serialization, deserialization, and validation of data. In this tutorial, we will use FastAPI to create a simple "Hello World" application. We test and run the application locally. FastAPI requires a ASGI server to run the application production such as Uvicorn.

Look at the intended semantics [1], and then read the implementation [2]. Can you figure out if the implementation is correct? Can you infer the possible limitations of the approach at glance? Can your async library actually handle being called with multiple event loops installed?

I have zero trust in this code and I have been bitten by fixes to this library that introduced deadlocks in my own code.

[1] https://github.com/django/asgiref#synchronous-code--threads.

[2] https://github.com/django/asgiref/blob/main/asgiref/sync.py#...

Using WebSockets in Django utilizes asynchronous Python and Django channels, making the process straightforward. Using Django channels, you can create an ASGI server, and then create a group where users can send text messages to all the other users in the group in real time. This way, you are not communicating with a particular user, but with a group, multiple users can be added.

If you're interested in the architecture itself I recommend you start by making yourself familiar with [ASGI specification](https://asgi.readthedocs.io/en/latest/) .

We switched to using strong typing derived from the asgiref for typing ASGI types, which makes Starlite the strongest type framework of its kind.

OK -

can we start considering binary files committed to a repo, even as data for tests, to be a huge red flag, and that the binary files themselves should instead be generated at testing time by source code that's stated as reviewable cleartext. This would make it much harder (though of course we can never really say "impossible") to embed a substantial payload in this way.

when binary files are part of a test suite, they are typically trying to illustrate some element of the program being tested, in this case a file that was incorrectly xz-encoded. Binary files like these weren't typed by hand, they will always ultimately come from something plaintext source.

Here's an example! My own SQLAlchemy repository has a few binary files in it! https://github.com/sqlalchemy/sqlalchemy/blob/main/test/bina... oh noes. Why are those files there? well in this case I just wanted to test that I can send large binary BLOBs into the database driver and I was lazy. This is actually pretty dumb, the two binary files here add 35K of useless crap to the source, and I could just as easily generate this binary data on the fly using a two liner that spits out random bytes. Anyone could see that two liner and know that it isn't embedding a malicious payload.

If I wanted to generate a poorly formed .xz file, I'd illustrate source code that generates random data, runs it through .xz, then applies "corruption" to it, like zeroing out the high bit of every byte. The process by which this occurs would be all reviewable in source code.

Besides, flama also provides support for SQL databases via SQLAlchemy, an SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL. Finally, flama also provides support for HTTP clients to perform requests via httpx, a next generation HTTP client for Python.

Alembic is a lightweight database migration tool for usage with SQLAlchemy. The term migration can be a little misleading, because in this context it doesn't mean to migrate to a different database in the sense of using a different version or a different type of database. In this context, migration refers to changes to the database schema: add a new column to a table, modify the type of an existing column, create a new index, etc..

To simplify this process, Cerbos developers have come up with adapters for popular Object-Relational Mapping (ORM) frameworks. You can check out for more details on the query plan repo - which also contains adapters for Prisma and SQLAlchemy - as well as a fully functioning application using Mongoose as its ORM.

That above pattern is one I've seen people do even recently, using the "select().c" attribute which from very early versions of SQLAlchemy is defined as "the columns from a subquery of the SELECT" ; this usage began raising deprecation warnings in 1.4 and is fully removed in 2.0 as it was a remnant of a much earlier version of SQLAlchemy. it will do exactly as you say, "make a subquery for each filter condition".

the moment you see SQLAlchemy doing something you see that seems "asinine", send an example to https://github.com/sqlalchemy/sqlalchemy/discussions and I will clarify what's going on, correct the usage so that the query you have is what you expect, and quite often we will add new warnings or documentation when we see people doing things we didn't anticipate.

The creator and lead maintainer of SQLAlchemy, one of the most popular and most used Python library for accessing databases (who doesn't?) gave a rather interesting response to PEP703.

If this doesn't ring any alarm bells I don't know what will.

> Basically for the moment the GIL-less idea would likely be burdensome for us and the fact that it's only an "option" seems to strongly imply major compatibility issues that we would not prefer.

https://github.com/sqlalchemy/sqlalchemy/discussions/10002#d...

Recently I discovered BigQuery public datasets - just over 200 datasets available for directly querying via SQL. I think this is a great thing! I can connect these direct to an analytics platform (we use Apache Superset which uses Python SQLAlchemy under the hood) for example and just start dashboarding.

When using python with sql databases like postgres or mariadb or SQLite you would use SQLAlchemy or another ORM of if you're feeling brave, you code it by hand. With ORMs you provide the address of your database and it connects for you, letting you use abstractions instead of writing all the SQL yourself (kind of analogous to using vlookups or index match instead of manually entering data).

Our tool we're going to be using for interfacing with the SQLite database is SQLAlchemy, a SQL toolkit that provides a unified API for various relational databases. If you installed FastAPI with pip install "fastapi[all]", SQLAlchemy is already part of your setup. but if you opted for FastAPI alone, you would need to install SQLAlchemy separately with pip install sqlalchemy.