Ce découplage des parties, et le fait d'être un "micro-framework" qui puisse être étendu pour couvrir exactement ce
qui est nécessaire, était une caractéristique clé que je voulais conserver.

Compte tenu de la simplicité de Flask, il semblait bien adapté à la création d'API. La prochaine chose à trouver était un "Django REST Framework" pour Flask.

/// check | "A inspiré **FastAPI** à"

          "interval": "",
          "legendFormat": "[{{drive}}:{{api}}]",
          "refId": "B"
        }
      ],
      "title": "Drive Latency (micro sec)",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "${DS_PROMETHEUS}"
      },
      "description": "",
      "fieldConfig": {

Kullanıcılar için otomatik API dökümantasyonu sunan bir web arayüzüne sahip olmalı.

///

### <a href="https://flask.palletsprojects.com" class="external-link" target="_blank">Flask</a>

Flask bir <abbr title="Mikro Framework: Micro Framework">mikro framework</abbr> olduğundan Django gibi framework'lerin aksine veritabanı entegrasyonu gibi Django ile gelen pek çok özelliği direkt barındırmaz.

		return nil, nil, errInvalidArgument
	}

	// prepare getElapsedTime() to calculate elapsed time since we started trying formatting disks.
	// All times are rounded to avoid showing milli, micro and nano seconds
	formatStartTime := time.Now().Round(time.Second)
	getElapsedTime := func() string {
		return time.Now().Round(time.Second).Sub(formatStartTime).String()
	}

	var (
		tries   int

با FastAPI شما تمام ویژگی های Pydantic را دراختیار دارید (زیرا FastAPI برای تمام بخش مدیریت دیتا بر اساس Pydantic عمل میکند):

* **خبری از گیج شدن نیست**:
    * هیچ <abbr title="micro-language">زبان خردی</abbr> برای یادگیری تعریف طرحواره های جدید وجود ندارد.
    * اگر تایپ های پایتون را میشناسید، نحوه استفاده از Pydantic را نیز میدانید.

Con **FastAPI** obtienes todas las características de **Pydantic** (dado que FastAPI está basado en Pydantic para todo el manejo de datos):

* **Sin dificultades para entender**:
    * No necesitas aprender un nuevo micro-lenguaje de definición de schemas.
    * Si sabes tipos de Python, sabes cómo usar Pydantic.

Com **FastAPI** você terá todos os recursos do **Pydantic** (já que FastAPI utiliza o Pydantic para todo o controle dos dados):

* **Sem pegadinhas**:
    * Sem novas definições de esquema de micro-linguagem para aprender.
    * Se você conhece os tipos do Python, você sabe como usar o Pydantic.

    try {
      // We get the result, even if collectOneValue is a no-op, so that we can fail fast.
      // We use getUninterruptibly over getDone as a micro-optimization, we know the future is done.
      collectOneValue(index, getUninterruptibly(future));
    } catch (ExecutionException e) {
      handleException(e.getCause());

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"

Be a micro-framework. Making it easy to mix and match the tools and parts needed.

Have a simple and easy to use routing system.

///

### <a href="https://requests.readthedocs.io" class="external-link" target="_blank">Requests</a>

 * thus paying for the cost of the expensive task.
 *
 * @author Dimitris Andreou
 * @since 13.0
 */
// TODO(user): switch to nano precision. A natural unit of cost is "bytes", and a micro precision
// would mean a maximum rate of "1MB/s", which might be small in some cases.
@Beta
@J2ktIncompatible
@GwtIncompatible
@ElementTypesAreNonnullByDefault
public abstract class RateLimiter {
  /**