* Fix logrotate config on GCI ([#29139](https://github.com/kubernetes/kubernetes/pull/29139), [@adityakali](https://github.com/adityakali))
* Do not query the metadata server to find out if running on GCE.  Retry metadata server query for gcr if running on gce. ([#28871](https://github.com/kubernetes/kubernetes/pull/28871), [@vishh](https://github.com/vishh))

crawler.hotthread.timeout=30s
# Type of hot thread monitoring (e.g., cpu).
crawler.hotthread.type=cpu
# Metadata fields to exclude from document content.
crawler.metadata.content.excludes=resourceName,X-Parsed-By,Content-Encoding.*,Content-Type.*,X-TIKA.*,X-FESS.*
# Mapping for document metadata names.
crawler.metadata.name.mapping=\
title=title:string\n\
Title=title:string\n\
dc:title=title:string\n\

# html

### Создание таблиц { #create-the-tables }

Далее мы добавим функцию, которая использует `SQLModel.metadata.create_all(engine)`, чтобы **создать таблицы** для всех *моделей-таблиц*.

{* ../../docs_src/sql_databases/tutorial001_an_py310.py ln[21:22] hl[21:22] *}

### Создание зависимости Session { #create-a-session-dependency }

### Create the Tables { #create-the-tables }

We then add a function that uses `SQLModel.metadata.create_all(engine)` to **create the tables** for all the *table models*.

{* ../../docs_src/sql_databases/tutorial001_an_py310.py ln[21:22] hl[21:22] *}

### Create a Session Dependency { #create-a-session-dependency }

### Die Tabellen erstellen { #create-the-tables }

Dann fügen wir eine Funktion hinzu, die `SQLModel.metadata.create_all(engine)` verwendet, um die **Tabellen für alle *Tabellenmodelle* zu erstellen**.

{* ../../docs_src/sql_databases/tutorial001_an_py310.py ln[21:22] hl[21:22] *}

///

**FastAPI**는 모두 Pydantic에 기반을 두고 있습니다.

이 모든 것은 [자습서 - 사용자 안내서](tutorial/index.md)에서 실제로 많이 보게 될 것입니다.

## 메타데이터 애너테이션이 있는 타입 힌트 { #type-hints-with-metadata-annotations }

파이썬에는 `Annotated`를 사용해 이러한 타입 힌트에 **추가 <dfn title="데이터에 대한 데이터, 여기서는 타입에 대한 정보(예: 설명)">메타데이터</dfn>**를 넣을 수 있는 기능도 있습니다.

`Annotated`는 `typing`에서 import할 수 있습니다.

因此，容器只消耗很少的資源，與直接執行行程相當（而虛擬機會消耗更多）。

容器也有其各自隔離的執行行程（通常只有一個行程）、檔案系統與網路，簡化部署、安全性與開發等。

## 什麼是容器映像 { #what-is-a-container-image }

容器是由容器映像啟動執行的。

容器映像是所有檔案、環境變數，以及在容器中應該執行的預設指令/程式的靜態版本。這裡的「靜態」意指容器映像不在執行，它只是被封裝的檔案與 metadata。

相對於儲存的靜態內容「容器映像」，「容器」通常指執行中的實例，也就是正在被執行的東西。

當容器啟動並執行時（自容器映像啟動），它可以建立或變更檔案、環境變數等。這些變更只會存在於該容器中，不會持久化回底層的容器映像（不會寫回磁碟）。

容器映像可類比為程式檔與其內容，例如 `python` 與某個 `main.py` 檔案。

/**
 * A transformer implementation for processing HTML documents using XPath expressions.
 * This class extends XpathTransformer to provide Fess-specific document processing capabilities
 * including content extraction, metadata processing, and robots tag handling.
 */
public class FessXpathTransformer extends XpathTransformer implements FessTransformer {

    /** Logger instance for this class */

	info := madmin.GetSysErrors(context.Background(), globalLocalNodeName)
	return madminSysErrors.NewJSONWith(&info), nil
}

// DeleteBucketMetadataHandler - Delete in memory bucket metadata
func (s *peerRESTServer) DeleteBucketMetadataHandler(mss *grid.MSS) (np grid.NoPayload, nerr *grid.RemoteErr) {
	bucketName := mss.Get(peerRESTBucket)
	if bucketName == "" {

    /**
     * Retrieves a paginated list of crawling information records based on the provided pager criteria.
     * The results are ordered by creation time in descending order and the pager is updated with
     * pagination metadata including total count and page number list.
     *
     * @param crawlingInfoPager the pager object containing search criteria and pagination settings
     * @return a list of CrawlingInfo entities matching the criteria
     */