swagger-editor VS SQLAlchemy

It has an online editor. You can easily play around with it and generate easy-to-use documentation.

After running the collection, I can see the API spec that was created and the mock server endpoint to test it. Looking at the rendered version on Swagger's OAS editor we can see pretty clearly this is a complete API that gets us exactly what we need.

While there’s not an officially supported Java SDK for Marqeta, building a Java client is quite straightforward, as the Core API is documented in both Swagger v2.0 and OpenAPI v3.0. The OpenAPI documentation is in beta, but it is generated directly from the API source code. To get a Java client, all we need to do is drop the OpenAPI yaml file into editor.swagger.io, modify the servers section to use the https://sandbox-api.marqeta.com/v3 as the URL, and tell it to generate a Java client.

Check out https://editor.swagger.io/ as a start point. In theory you should be able to generate a client for any swagger complient api and plug in your own auth and custom logic.

I would suggest using the swagger editor: https://editor.swagger.io/

Paste your json into https://editor.swagger.io and it will ask you if you want to convert it to yaml

Sure. You can use the editor from here for instance to define your endpoints and the data received and returned. By looking at the preloaded example you can figure out most of what you need to know about openapi. But if you need more info, the official documentation is pretty good.

Optional: If you make any changes to the plugin instructions or metadata models, you can also copy the contents of main.py into the main main.py file. This will allow you to access the openapi.json at http://0.0.0.0:8000/sub/openapi.json when you run the app locally. You can convert from JSON to YAML format with Swagger Editor. Alternatively, you can replace the openapi.yaml file with an openapi.json file.

I created another issue, this time quoting directly from swagger.io, showing screenshots from editor.swagger.io validation to prove that the library is creating invalid OpenAPI descriptions and that my suggestion creates valid ones, rephrasing the entire problem from a slightly different angle. I asked that if he decides to close the issue, to please not delete it so that it serves as documentation for others.

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.