This decoupling of parts, and being a "microframework" that could be extended to cover exactly what is needed was a key feature that I wanted to keep.

Given the simplicity of Flask, it seemed like a good match for building APIs. The next thing to find was a "Django REST Framework" for Flask.

!!! check "Inspired **FastAPI** to"

This can be very useful for setting up **resources** that you need to use for the whole app, and that are **shared** among requests, and/or that you need to **clean up** afterwards. For example, a database connection pool, or loading a shared machine learning model.

## Use Case

Let's start with an example **use case** and then see how to solve it with this.

Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖

* Then check that the tests **pass** after the PR. ✅

* Many PRs don't have tests, you can **remind** them to add tests, or you can even **suggest** some tests yourself. That's one of the things that consume most time and you can help a lot with that.

* Then also comment what you tried, that way I'll know that you checked it. 🤓

## Create a Pull Request

### FastAPI Data Filtering

Now, for FastAPI, it will see the return type and make sure that what you return includes **only** the fields that are declared in the type.

You can see those schemas because they were declared with the models in the app.

That information is available in the app's **OpenAPI schema**, and then shown in the API docs (by Swagger UI).

And that same information from the models that is included in OpenAPI is what can be used to **generate the client code**.

### Generate a TypeScript Client

Now that we have the app with the models, we can generate the client code for the frontend.

    ```

The last `CommonQueryParams`, in:

```Python
... Depends(CommonQueryParams)
```

...is what **FastAPI** will actually use to know what is the dependency.

From it is that FastAPI will extract the declared parameters and that is what FastAPI will actually call.

---

In this case, the first `CommonQueryParams`, in:

=== "Python 3.8+"

    ```Python

## Containers and Processes

A **container image** normally includes in its metadata the default program or command that should be run when the **container** is started and the parameters to be passed to that program. Very similar to what would be if it was in the command line.

When a **container** is started, it will run that command/program (although you can override it and make it run a different command/program).

Now create Pydantic *models* (schemas) that will be used when reading data, when returning it from the API.

For example, before creating an item, we don't know what will be the ID assigned to it, but when reading it (when returning it from the API) we will already know its ID.

The same way, when reading a user, we can now declare that `items` will contain the items that belong to this user.

You could create an API with a *path operation* that could trigger a request to an *external API* created by someone else (probably the same developer that would be *using* your API).

The process that happens when your API app calls the *external API* is named a "callback". Because the software that the external developer wrote sends a request to your API and then your API *calls back*, sending a request to an *external API* (that was probably created by the same developer).

When fetching certificates, ztunnel will authenticate to the CA with its own identity, but request the identity of another workload.
Critically, the CA must enforce that the ztunnel has permission to request that identity.
Requests for identities not running on the node are rejected.
This is critical to ensure that a compromised node does not compromise the entire mesh.