flask-ligand-example VS SQLAlchemy

All example code in this section is copied straight from the example project source code.

We'll use the Flask-SQLAlchemy package to manage the database. This package provides SQLAlchemy support to Flask projects. That includes the Python SQL toolkit and the ORM.

SQLite for the database

I'm not familiar with the library either, but that seems to be a SQL expression executed on the database server. It's basically a copy-paste from the official documentation[0]. So no, not a lambda expression, because it's not computed in Python.

As to the extra parentheses: I bet that's a force-of-habit thing to prevent potential issues. For example, it seems Sqlite requires them for exactly this kind of default definition[1]. It could also read to nasty bugs when the lack of parentheses in the resulting SQL could result in a different parse than expected[2]. Adding them just-to-be-safe isn't the worst thing to do.

[0]: https://docs.sqlalchemy.org/en/13/core/metadata.html

[1]: https://github.com/sqlalchemy/sqlalchemy/issues/4474

[2]: https://github.com/sqlalchemy/sqlalchemy/issues/5344

Depending on the progress of your application, you may want to migrate to another framework faster than Django. Django’s ORM is not exactly the fastest out there, and, at the time of writing, it is not asynchronous. You might want to consider giving sqlalchemy, ponyorm a try.

SQLAlchemy is a widely used database toolkit for Python, providing a SQL abstraction layer covering most, if not all, your relational database needs. It's often used together with popular frameworks such as Flask and FastAPI, using either its Core library and/or its ORM components.

In SQLAlchemy, models representing database tables typically subclass sqlalchemy.orm.DeclarativeBase (this class supersedes the sqlalchemy.orm.declarative_base function). Accordingly, the abstract base class in this database wrapper component is a sqlalchemy.orm.DeclarativeBase subclass, accompanied by another custom base class providing additional dunder methods.

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..