* Restarts
* Replication (the number of processes running)
* Memory
* Previous steps before starting

## Memory

If you run **a single process per container** you will have a more or less well-defined, stable, and limited amount of memory consumed by each of those containers (more than one if they are replicated).

### Memory per Process

Now, when the program loads things in memory, for example, a machine learning model in a variable, or the contents of a large file in a variable, all that **consumes a bit of the memory (RAM)** of the server.

* You don't have to use `File()` in the default value of the parameter.
* It uses a "spooled" file:
    * A file stored in memory up to a maximum size limit, and after passing this limit it will be stored in disk.
* This means that it will work well for large files like images, videos, large binaries, etc. without consuming all the memory.
* You can get metadata from the uploaded file.

* 403: `status.HTTP_403_FORBIDDEN`
* etc.

It can be convenient to quickly access HTTP (and WebSocket) status codes in your app, using autocompletion for the name without having to remember the integer status codes by memory.

Read more about it in the [FastAPI docs about Response Status Code](https://fastapi.tiangolo.com/tutorial/response-status-code/).

## Example

```python
from fastapi import FastAPI, status

app = FastAPI()

# Server Workers - Gunicorn with Uvicorn

Let's check back those deployment concepts from before:

* Security - HTTPS
* Running on startup
* Restarts
* **Replication (the number of processes running)**
* Memory
* Previous steps before starting

Up to this point, with all the tutorials in the docs, you have probably been running a **server program** like Uvicorn, running a **single process**.

```

!!! tip
    The app above is a minimal and simple example to demonstrate how to handle and broadcast messages to several WebSocket connections.

    But keep in mind that, as everything is handled in memory, in a single list, it will only work while the process is running, and will only work with a single process.

## Caveat

If you need to perform heavy background computation and you don't necessarily need it to be run by the same process (for example, you don't need to share memory, variables, etc), you might benefit from using other bigger tools like <a href="https://docs.celeryq.dev" class="external-link" target="_blank">Celery</a>.

    ├── schemas.py
    └── tests
        ├── __init__.py
        └── test_sql_app.py
```

## Create the new database session

First, we create a new database session with the new database.

We'll use an in-memory database that persists during the tests instead of the local file `sql_app.db`.

But the rest of the session code is more or less the same, we just copy it.

```Python hl_lines="8-13"

If you have a file-like object (e.g. the object returned by `open()`), you can create a generator function to iterate over that file-like object.

That way, you don't have to read it all first in memory, and you can pass that generator function to the `StreamingResponse`, and return it.

This includes many libraries to interact with cloud storage, video processing, and others.

This is the basic idea. But you will probably want to take care of some additional things, like:

* Security - HTTPS
* Running on startup
* Restarts
* Replication (the number of processes running)
* Memory
* Previous steps before starting