h.mu.RLock()
	defer h.mu.RUnlock()

	b, err := json.MarshalIndent(h, "", "  ")
	if err != nil {
		writer.Write([]byte(err.Error()))
		return
	}
	writer.Write(b)
}

// toHealingDisk converts the information to madmin.HealingDisk
func (h *healingTracker) toHealingDisk() madmin.HealingDisk {
	h.mu.RLock()
	defer h.mu.RUnlock()

	return madmin.HealingDisk{
		ID:                h.ID,

{* ../../docs_src/dataclasses_/tutorial001_py310.py hl[1,6:11,18:19] *}

这仍然得益于 **Pydantic**，因为它对 [`dataclasses` 的内置支持](https://docs.pydantic.dev/latest/concepts/dataclasses/#use-of-stdlib-dataclasses-with-basemodel)。

因此，即便上面的代码没有显式使用 Pydantic，FastAPI 也会使用 Pydantic 将那些标准数据类转换为 Pydantic 风格的 dataclasses。

并且，它仍然支持以下功能：

* 数据验证
* 数据序列化
* 数据文档等

The `--reload` option consumes much more resources, is more unstable, etc.

It helps a lot during **development**, but you **shouldn't** use it in **production**.

///

## Deployment Concepts { #deployment-concepts }

These examples run the server program (e.g Uvicorn), starting **a single process**, listening on all the IPs (`0.0.0.0`) on a predefined port (e.g. `80`).

import org.opensearch.common.unit.Fuzziness;
import org.opensearch.index.query.QueryBuilder;
import org.opensearch.index.query.QueryBuilders;

/**
 * Query command implementation for handling fuzzy search queries.
 * This class converts Lucene FuzzyQuery objects into OpenSearch fuzzy query builders,
 * supporting configurable fuzzy matching parameters like edit distance and expansions.
 *
 */
public class FuzzyQueryCommand extends QueryCommand {

Uygulamaları deploy ederken, çok çekirdekten (multiple cores) faydalanmak ve daha fazla request'i karşılayabilmek için büyük olasılıkla process replikasyonu (birden fazla process) isteyeceksiniz.

[Daha önceki Deployment Concepts](concepts.md) bölümünde gördüğünüz gibi, kullanabileceğiniz birden fazla strateji var.

Burada, `fastapi` komutunu kullanarak ya da `uvicorn` komutunu doğrudan çalıştırarak worker process'lerle Uvicorn'u nasıl kullanacağınızı göstereceğim.

This page is intended as a summary of MinIO Erasure Coding. For a more complete explanation, see <https://docs.min.io/community/minio-object-store/operations/concepts/erasure-coding.html>.

## Overview

MinIO supports two storage classes, Reduced Redundancy class and Standard class. These classes can be defined using environment variables

/// tip | Astuce

L'attribut `model_config` est utilisé uniquement pour la configuration Pydantic. Vous pouvez en lire davantage ici : [Pydantic : Concepts : Configuration](https://docs.pydantic.dev/latest/concepts/config/).

///

Ici, nous définissons la configuration `env_file` à l'intérieur de votre classe Pydantic `Settings` et lui attribuons le nom du fichier dotenv que nous voulons utiliser.

        assertArgumentNotNull("obj", obj);

        final byte[] binary = fromObjectToBinary(obj);
        return fromBinaryToObject(binary);
    }

    /**
     * Converts an object to a byte array.
     *
     * @param obj the object to serialize (must not be {@literal null})
     * @return the byte array of the object
     */

    public static String toHexString(final int val, final int size) {
        final char[] c = new char[size];
        toHexChars(val, c, 0, size);
        return new String(c);
    }

    /**
     * Converts a long value to a hexadecimal string representation with specified padding.
     *
     * @param val the long value to convert to hexadecimal

{* ../../docs_src/dataclasses_/tutorial001_py310.py hl[1,6:11,18:19] *}

이는 **Pydantic** 덕분에 여전히 지원되는데, Pydantic이 [`dataclasses`에 대한 내부 지원](https://docs.pydantic.dev/latest/concepts/dataclasses/#use-of-stdlib-dataclasses-with-basemodel)을 제공하기 때문입니다.

따라서 위 코드처럼 Pydantic을 명시적으로 사용하지 않더라도, FastAPI는 Pydantic을 사용해 표준 dataclasses를 Pydantic의 dataclasses 변형으로 변환합니다.

그리고 물론 다음과 같은 기능도 동일하게 지원합니다:

* 데이터 검증