## Changelog since v1.18.13

## Changes by Kind

### Feature

- Add a new flag to set priority for the kubelet on Windows nodes so that workloads cannot overwhelm the node there by disrupting kubelet process. ([#96158](https://github.com/kubernetes/kubernetes/pull/96158), [@ravisantoshgudimetla](https://github.com/ravisantoshgudimetla)) [SIG Node]

### Bug or Regression

    - [Deprecation of klog specific flags](#deprecation-of-klog-specific-flags)
    - [Software Supply Chain SLSA Level 1 Compliance in the Kubernetes Release Process](#software-supply-chain-slsa-level-1-compliance-in-the-kubernetes-release-process)
    - [IPv4/IPv6 Dual-stack Networking graduates to GA](#ipv4ipv6-dual-stack-networking-graduates-to-ga)

func encodeDirObject(object string) string {
	if HasSuffix(object, slashSeparator) {
		return strings.TrimSuffix(object, slashSeparator) + globalDirSuffix
	}
	return object
}

// Reverse process of encodeDirObject()
func decodeDirObject(object string) string {
	if HasSuffix(object, globalDirSuffix) {
		return strings.TrimSuffix(object, globalDirSuffix) + slashSeparator
	}
	return object
}

- Kubelet.exe on Windows now checks that the process running as administrator and the executing user account is listed in the built-in administrators group.  This is the equivalent to checking the process is running as uid 0. ([#96616](https://github.com/kubernetes/kubernetes/pull/96616), [@perithompson](https://github.com/perithompson)) [SIG Node and Windows]

Mit **FastAPI** können Sie die Vorteile der Nebenläufigkeit nutzen, die in der Webentwicklung weit verbreitet ist (derselbe Hauptvorteil von NodeJS).

Sie können aber auch die Vorteile von Parallelität und Multiprocessing (mehrere Prozesse werden parallel ausgeführt) für **CPU-lastige** Workloads wie in Systemen für maschinelles Lernen nutzen.

The `string()` method on response body is convenient and efficient for small documents. But if the response body is large (greater than 1 MiB), avoid `string()` because it will load the entire document into memory. In that case, prefer to process the body as a stream.

=== ":material-language-kotlin: Kotlin"
    ```kotlin
      private val client = OkHttpClient()

      fun run() {
        val request = Request.Builder()

### Um Processo por Contêiner { #one-process-per-container }

Nesse tipo de cenário, provavelmente você desejará ter **um único processo (Uvicorn) por contêiner**, pois já estaria lidando com a replicação no nível do cluster.

     */

    public int getNodeType() throws UnknownHostException {
        checkData();
        return nodeType;
    }

    /**
     * Determines if this address in the process of being deleted.
     *
     * @return true if the address is being deleted, false otherwise
     * @throws UnknownHostException if the host cannot be resolved to find out.
     */

labels.dayForCleanup=Delete documents older than specified days
labels.crawlingThreadCount=Concurrent Crawling Count
labels.boost=Boost
labels.crawlingConfigName=Name
labels.crawlingConfigPath=Path to Crawl
labels.processType=Process Type
labels.parameters=Parameters
labels.designFile=File to Upload
labels.bulkFile=Bulk File
labels.appendQueryParameter=Append Search Parameters
labels.configId=Config ID
labels.configParameter=Config Parameter

* Return apiserver panics as 500 errors instead terminating the apiserver process. ([#68001](https://github.com/kubernetes/kubernetes/pull/68001), [@sttts](https://github.com/sttts))