Par exemple, il était clair que l'idéal était de se baser sur les annotations de type Python standard.

De plus, la meilleure approche était d'utiliser des normes déjà existantes.

Ainsi, avant même de commencer à coder **FastAPI**, j'ai passé plusieurs mois à étudier les spécifications d'OpenAPI, JSON Schema, OAuth2, etc. Comprendre leurs relations, leurs similarités et leurs différences.

```Python hl_lines="6-7"
{!../../../docs_src/path_params/tutorial001.py!}
```

El valor del parámetro de path `item_id` será pasado a tu función como el argumento `item_id`.

Entonces, si corres este ejemplo y vas a <a href="http://127.0.0.1:8000/items/foo" class="external-link" target="_blank">http://127.0.0.1:8000/items/foo</a>, verás una respuesta de:

```JSON
{"item_id":"foo"}
```

    * Si estás comparando Uvicorn, compáralo con los servidores de aplicaciones Daphne, Hypercorn, uWSGI, etc.
* **Starlette**:
    * Tendrá el siguiente mejor desempeño, después de Uvicorn. De hecho, Starlette usa Uvicorn para correr. Por lo tanto, probablemente sólo pueda volverse "más lento" que Uvicorn al tener que ejecutar más código.

        # GPU:
        *manylinux*)     LARGEST_OK_SIZE=580 ;;
        # Unknown:
        *)
            echo "The wheel's name is in an unknown format."
            exit 1
            ;;
    esac
    # >&3 forces output in bats even if the test passes. See
    # https://bats-core.readthedocs.io/en/stable/writing-tests.html#printing-to-the-terminal
    echo "# Size of $TF_WHEEL is $WHEEL_MEGABYTES / $LARGEST_OK_SIZE megabytes." >&3

      }
    }

    throw new IllegalArgumentException(
        Platform.format("key %s not found in entries %s", newEntry.getKey(), expected));
  }

  /**
   * Wrapper for {@link Map#get(Object)} that forces the caller to pass in a key of the same type as
   * the map. Besides being slightly shorter than code that uses {@link #getMap()}, it also ensures
   * that callers don't pass an {@link Entry} by mistake.
   */

When deploying applications you will probably want to have some **replication of processes** to take advantage of **multiple cores** and to be able to handle more requests.

As you saw in the previous chapter about [Deployment Concepts](concepts.md){.internal-link target=_blank}, there are multiple strategies you can use.

      }
    }

    throw new IllegalArgumentException(
        Platform.format("key %s not found in entries %s", newEntry.getKey(), expected));
  }

  /**
   * Wrapper for {@link Map#get(Object)} that forces the caller to pass in a key of the same type as
   * the map. Besides being slightly shorter than code that uses {@link #getMap()}, it also ensures
   * that callers don't pass an {@link Entry} by mistake.
   */

streaming compression due to its stability and performance.

This algorithm is specifically optimized for machine generated content.
Write throughput is typically at least 500MB/s per CPU core,
and scales with the number of available CPU cores.
Decompression speed is typically at least 1GB/s.

This means that in cases where raw IO is below these numbers
compression will not only reduce disk usage but also help increase system throughput.

// record is formatted as:
//
//	"%d %s=%s\n" % (size, key, value)
//
// Keys and values should be UTF-8, but the number of bad writers out there
// forces us to be a more liberal.
// Thus, we only reject all keys with NUL, and only reject NULs in values
// for the PAX version of the USTAR string fields.
// The key must not contain an '=' character.