## Benchmarks and speed

When you check the benchmarks, it is common to see several tools of different types compared as equivalent.

Specifically, to see Uvicorn, Starlette and FastAPI compared together (among many other tools).

The simpler the problem solved by the tool, the better performance it will get. And most of the benchmarks don't test the additional features provided by the tool.

The hierarchy is like:

* <a href="https://pgjones.gitlab.io/hypercorn/" class="external-link" target="_blank">Hypercorn</a>: an ASGI server compatible with HTTP/2 and Trio among other features.
* <a href="https://github.com/django/daphne" class="external-link" target="_blank">Daphne</a>: the ASGI server built for Django Channels.

## Server Machine and Server Program

!!! check "Inspired **FastAPI** to"
    Find ways to get great performance.

    Along with Hug (as Hug is based on Falcon) inspired **FastAPI** to declare a `response` parameter in functions.

    Although in FastAPI it's optional, and is used mainly to set headers, cookies, and alternative status codes.

When you run **multiple processes** of the same API program, they are commonly called **workers**.

### Worker Processes and Ports

---

Here's how to help others with questions (in discussions or issues):

### Understand the question

* Check if you can understand what is the **purpose** and use case of the person asking.

* Then check if the question (the vast majority are questions) is **clear**.

        By asking questions in a structured way (following this) it will be much easier to help you.

        And there's a high chance that you will find the solution along the way and you won't even have to submit it and wait for an answer. 😎

That, plus the simple fact that Python is the main language for **Data Science**, Machine Learning and especially Deep Learning, make FastAPI a very good match for Data Science / Machine Learning web APIs and applications (among many others).

To see how to achieve this parallelism in production see the section about [Deployment](deployment/index.md){.internal-link target=_blank}.

## `async` and `await`

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

```Python
from starlette.exceptions import HTTPException as StarletteHTTPException
```

### Re-use **FastAPI**'s exception handlers

If you want to use the exception along with the same default exception handlers from  **FastAPI**, You can import and re-use the default exception handlers from `fastapi.exception_handlers`:

```Python hl_lines="2-5  15  21"

Common ORMs are for example: Django-ORM (part of the Django framework), SQLAlchemy ORM (part of SQLAlchemy, independent of framework) and Peewee (independent of framework), among others.

Here we will see how to work with **SQLAlchemy ORM**.

In a similar way you could use any other ORM.

!!! tip
    There's an equivalent article using Peewee here in the docs.

## File structure