name: Issue Manager

on:
  schedule:
    - cron: "10 3 * * *"
  issue_comment:
    types:
      - created
  issues:
    types:
      - labeled
  pull_request_target:
    types:
      - labeled
  workflow_dispatch:

jobs:
  issue-manager:
    if: github.repository_owner == 'tiangolo'
    runs-on: ubuntu-latest
    steps:
      - name: Dump GitHub context
        env:

manager = ConnectionManager()


@app.get("/")
async def get():
    return HTMLResponse(html)


@app.websocket("/ws/{client_id}")
async def websocket_endpoint(websocket: WebSocket, client_id: int):
    await manager.connect(websocket)
    try:
        while True:
            data = await websocket.receive_text()

### Multiple Processes - An Example

In this example, there's a **Manager Process** that starts and controls two **Worker Processes**.

This Manager Process would probably be the one listening on the **port** in the IP. And it would transmit all the communication to the worker processes.

manager = ConnectionManager()


@app.get("/")
async def get():
    return HTMLResponse(html)


@app.websocket("/ws/{client_id}")
async def websocket_endpoint(websocket: WebSocket, client_id: int):
    await manager.connect(websocket)
    try:
        while True:
            data = await websocket.receive_text()

* HAProxy
    * С дополнительным компонентом типа Certbot для обновления сертификатов
* Kubernetes с Ingress Controller похожим на Nginx
    * С дополнительным компонентом типа cert-manager для обновления сертификатов
* Использование услуг облачного провайдера (читайте ниже 👇)

Sie erfordern in der Regel komplexere Konfigurationen und einen Nachrichten-/Job-Queue-Manager wie RabbitMQ oder Redis, ermöglichen Ihnen jedoch die Ausführung von Hintergrundtasks in mehreren Prozessen und insbesondere auf mehreren Servern.

But Gunicorn supports working as a **process manager** and allowing users to tell it which specific **worker process class** to use. Then Gunicorn would start one or more **worker processes** using that class.

And **Uvicorn** has a **Gunicorn-compatible worker class**.

@asynccontextmanager
async def contextmanager_in_threadpool(
    cm: ContextManager[_T],
) -> AsyncGenerator[_T, None]:
    # blocking __exit__ from running waiting on a free thread
    # can create race conditions/deadlocks if the context manager itself
    # has its own internal pool (e.g. a database connection pool)
    # to avoid this we let __exit__ run without a capacity limit
    # since we're creating a new limiter for each call, any non-zero limit

    * 証明書の更新を自動的に処理 ✨
* Caddy
    * 証明書の更新を自動的に処理 ✨
* Nginx
    * 証明書更新のためにCertbotのような外部コンポーネントを使用
* HAProxy
    * 証明書更新のためにCertbotのような外部コンポーネントを使用
* Nginx のような Ingress Controller を持つ Kubernetes
    * 証明書の更新に cert-manager のような外部コンポーネントを使用
* クラウド・プロバイダーがサービスの一部として内部的に処理（下記を参照👇）

もう1つの選択肢は、HTTPSのセットアップを含んだより多くの作業を行う**クラウド・サービス**を利用することです。 このサービスには制限があったり、料金が高くなったりする可能性があります。しかしその場合、TLS Termination Proxyを自分でセットアップする必要はないです。

次の章で具体例をいくつか紹介します。

So, in this case, you **would not** want to have a process manager like Gunicorn with Uvicorn workers, or Uvicorn using its own Uvicorn workers. You would want to have just a **single Uvicorn process** per container (but probably multiple containers).