postgres-websockets VS SQLAlchemy

At work, we've finally replaced a large part of a custom (mostly-)web backend with PostgREST recently, and that's quite a relief: considerably less code to maintain in that project now, and that was a rather awkward code. Something akin to PostgREST's "Embedding with Top-level Filtering" [1] had to be provided for all the tables, with OpenAPI schema and a typed API (Haskell + Servant); I avoided manually writing it all down, but at the cost of poking framework internals, and maintainability suffered. It was particularly annoying that the code doesn't really do anything useful, except for standing between a database and an HTTP client, and simply mimics the database anyway. Whenever a change had to be introduced, it was introduced into the database, the backend, and the frontend simultaneously, so it wasn't even useful for some kind of compatibility.

Now PostgREST handles all that, and only a few less trivial endpoints are handled by a custom backend (including streaming, which I'm considering replacing with postgrest-websocket [2] at some point).

During the switch to PostgREST, the encountered minor issues were those with inherited tables (had to set a bunch of computed/virtual columns [3] in order to "embed" those), and with a bug on filtering using such relations (turned out it was an already-fixed regression [4], so an update helped). Also a couple of helper stored procedures (to use via /rpc/) for updates in multiple tables at once (many-to-many relationships, to edit entities along with their relationships, using fewer requests) were added (though the old custom backend didn't have that), the security policies were set from the beginning, the frontend was rewritten (which allowed to finally switch without adding more work), so it was only left to cleanup the backend.

Not using views, since as mentioned above, database changes usually correspond to frontend changes, and the API doesn't have to be that stable yet.

Happy with it so far.

[1] https://postgrest.org/en/stable/api.html#embedding-with-top-...

[2] https://github.com/diogob/postgres-websockets

[3] https://postgrest.org/en/stable/api.html#computed-virtual-co...

[4] https://github.com/PostgREST/postgrest/issues/2530

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.