exclusive CPU cores.  This helps performance in a variety of use-cases, including network latency sensitive applications, as well as applications that benefit from CPU cache residency.  Next, [Huge Pages](https://kubernetes.io/docs/tasks/manage-hugepages/scheduling-hugepages/), which allows pods to consume either 2Mi or 1Gi Huge Pages.  This benefits applications that consume large amounts of memory.  Use of Huge Pages is a common tuning recommendation for databases and JVMs.  Finally, the [Device...

Die Verwendung von Linux-Containern bietet mehrere Vorteile, darunter **Sicherheit**, **Replizierbarkeit**, **Einfachheit** und andere.

/// tip | Tipp

Sie haben es eilig und kennen sich bereits aus? Springen Sie zum [`Dockerfile` unten 👇](#ein-docker-image-fur-fastapi-erstellen).

///

<Details>
<summary>Dockerfile-Vorschau 👀</summary>

```Dockerfile
FROM python:3.9

WORKDIR /code

Und in den nächsten Abschnitten wird davon ausgegangen, dass Sie es bereits gelesen haben und dass Sie diese Haupt-Ideen kennen.

## Externe Kurse

Sie verfügt über eine *Pfadoperation*, die einen `Invoice`-Body empfängt, und einen Query-Parameter `callback_url`, der die URL für den Callback enthält.

Dieser Teil ist ziemlich normal, der größte Teil des Codes ist Ihnen wahrscheinlich bereits bekannt:

{* ../../docs_src/openapi_callbacks/tutorial001.py hl[9:13,36:53] *}

/// tip | Tipp

   *     associated FinalizableReferenceQueue is no longer referenced.
   */
  private boolean finalizeReference(Reference<?> reference, Method finalizeReferentMethod) {
    /*
     * This is for the benefit of phantom references. Weak and soft references will have already
     * been cleared by this point.
     */
    reference.clear();

    if (reference == frqReference) {
      /*

   * types (or if the type is a non-variable type, like {@code String}), then code should almost
   * never use this method, preferring instead to call {@code requireNonNull} so as to benefit from
   * its runtime check.
   *
   * <p>An example use case for this method is in implementing an {@code Iterator<T>} whose {@code

 * instance for non-daemon threads.
 *
 * The task runner is also responsible for releasing held threads when the library is unloaded.
 * This is for the benefit of container environments that implement code unloading.
 *
 * Most applications should share a process-wide [TaskRunner] and use queues for per-client work.
 */
class TaskRunner(
  val backend: Backend,

Keep in mind that this means that the whole contents will be stored in memory. This will work well for small files.

But there are several cases in which you might benefit from using `UploadFile`.

## File Parameters with `UploadFile` { #file-parameters-with-uploadfile }

Define a file parameter with a type of `UploadFile`:

{* ../../docs_src/request_files/tutorial001_an_py39.py hl[14] *}

   * required to be a {@link BufferedWriter} in order to allow implementations to simply delegate to
   * {@link #openStream()} when the stream returned by that method does not benefit from additional
   * buffering. This method returns a new, independent writer each time it is called.
   *
   * <p>The caller is responsible for ensuring that the returned writer is closed.
   *

## Caveat { #caveat }

If you need to perform heavy background computation and you don't necessarily need it to be run by the same process (for example, you don't need to share memory, variables, etc), you might benefit from using other bigger tools like <a href="https://docs.celeryq.dev" class="external-link" target="_blank">Celery</a>.