OkHttpClient.Builder()
      .connectTimeout(5, TimeUnit.SECONDS)
      .writeTimeout(5, TimeUnit.SECONDS)
      .readTimeout(5, TimeUnit.SECONDS)
      .callTimeout(10, TimeUnit.SECONDS)
      .build()

  fun run() {
    val request =
      Request.Builder()
        .url("http://httpbin.org/delay/2") // This URL is served with a 2 second delay.
        .build()

  //   * increase no more than 4 pods per 60 seconds
  //   * double the number of pods per 60 seconds
  // No stabilization is used.
  // +optional
  optional HPAScalingRules scaleUp = 1;

  // scaleDown is scaling policy for scaling Down.
  // If not set, the default value is to allow to scale down to minReplicas pods, with a
  // 300 second stabilization window (i.e., the highest recommendation for

        }

        override fun onMessage(
          webSocket: WebSocket,
          bytes: ByteString,
        ) {
          webSocket.send(bytes)
        }

        override fun onMessage(
          webSocket: WebSocket,
          text: String,
        ) {
          webSocket.send(text)
        }

        override fun onClosing(
          webSocket: WebSocket,
          code: Int,
          reason: String,

    //      last modified: 105 seconds ago
    //             served:   5 seconds ago
    //   default lifetime: (105 - 5) / 10 = 10 seconds
    //            expires:  10 seconds from served date = 5 seconds from now
    server.enqueue(
      MockResponse.Builder()
        .addHeader("Last-Modified: " + formatDate(-105, TimeUnit.SECONDS))
        .addHeader("Date: " + formatDate(-5, TimeUnit.SECONDS))

En particulier, on voit Uvicorn, Starlette et FastAPI comparés (parmi de nombreux autres outils).

Plus le problème résolu par un outil est simple, mieux seront les performances obtenues. Et la plupart des tests de performance ne prennent pas en compte les fonctionnalités additionnelles fournies par les outils.

La hiérarchie est la suivante :

* **Uvicorn** : un serveur ASGI

      if (requestCaching.maxAgeSeconds != -1) {
        freshMillis = minOf(freshMillis, SECONDS.toMillis(requestCaching.maxAgeSeconds.toLong()))
      }

      var minFreshMillis: Long = 0
      if (requestCaching.minFreshSeconds != -1) {
        minFreshMillis = SECONDS.toMillis(requestCaching.minFreshSeconds.toLong())
      }

      var maxStaleMillis: Long = 0

  ): RecordedRequest? = requestQueue.poll(timeout, unit)

  /**
   * Scripts [response] to be returned to a request made in sequence. The first request is
   * served by the first enqueued response; the second request by the second enqueued response; and
   * so on.
   *
   * @throws ClassCastException if the default dispatcher has been
   * replaced with [setDispatcher][dispatcher].
   */

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

second_user_data = {
    "id": 4,
    "name": "Mary",
    "joined": "2018-11-30",
}

my_second_user: User = User(**second_user_data)
```

!!! info
    `**second_user_data` bedeutet:

  // object refers to a metric describing a single kubernetes object
  // (for example, hits-per-second on an Ingress object).
  // +optional
  optional ObjectMetricSource object = 2;

  // pods refers to a metric describing each pod in the current scale target
  // (for example, transactions-processed-per-second).  The values will be
  // averaged together before being compared to the target value.
  // +optional

对于这些情况，你可以使用Python的技术，将 `dict` 与 `**dict_to_unpack` 解包：
```Python
old_dict = {
    "old key": "old value",
    "second old key": "second old value",
}
new_dict = {**old_dict, "new key": "new value"}
```

这里， new_dict 将包含来自 old_dict 的所有键值对加上新的键值对：
```python
{
    "old key": "old value",
    "second old key": "second old value",
    "new key": "new value",
}
```
您可以使用该技术在路径操作中重用一些预定义的响应，并将它们与其他自定义响应相结合。
**例如：**