Você pode combinar *operações de rota* normais do FastAPI com GraphQL na mesma aplicação.

/// tip | "Dica"

**GraphQL** resolve alguns casos de uso muito específicos.

Ele tem **vantagens** e **desvantagens** quando comparado a **web APIs** comuns.

Certifique-se de avaliar se os **benefícios** para o seu caso de uso compensam as **desvantagens**. 🤓

///

## Bibliotecas GraphQL

import okhttp3.Headers
import okhttp3.HttpUrl
import okhttp3.HttpUrl.Companion.toHttpUrlOrNull
import okhttp3.internal.platform.Platform
import okio.Buffer

/** An HTTP request that came into the mock web server. */
@ExperimentalOkHttpApi
class RecordedRequest(
  val requestLine: String,
  /** All headers. */
  val headers: Headers,
  /**

import java.util.concurrent.TimeUnit
import java.util.concurrent.atomic.AtomicLong
import java.util.concurrent.atomic.AtomicReference
import okhttp3.internal.USER_AGENT
import okio.ByteString

/**
 * Exercises the web socket implementation against the
 * [Autobahn Testsuite](http://autobahn.ws/testsuite/).
 */
class AutobahnTester {
  val client = OkHttpClient()

  private fun newWebSocket(
    path: String,

```Python hl_lines="8-9  11  14"
{!../../docs_src/middleware/tutorial001.py!}
```

/// tip | "Dica"

Tenha em mente que cabeçalhos proprietários personalizados podem ser adicionados <a href="https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers" class="external-link" target="_blank">usando o prefixo 'X-'</a>.

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* **深度学习**: 这是机器学习的一个子领域，同样适用。只是没有一个数字的电子表格可以相乘，而是一个庞大的数字集合，在很多情况下，你需要使用一个特殊的处理器来构建和使用这些模型。

### 并发 + 并行: Web + 机器学习

使用 **FastAPI**，您可以利用 Web 开发中常见的并发机制的优势（NodeJS 的主要吸引力）。

并且，您也可以利用并行和多进程（让多个进程并行运行）的优点来处理与机器学习系统中类似的 **CPU 密集型** 工作。

这一点，再加上 Python 是**数据科学**、机器学习（尤其是深度学习）的主要语言这一简单事实，使得 **FastAPI** 与数据科学/机器学习 Web API 和应用程序（以及其他许多应用程序）非常匹配。

了解如何在生产环境中实现这种并行性，可查看此文 [Deployment](deployment/index.md){.internal-link target=_blank}。

    public static final String LABELS_crawling_info_WebFsCrawlEndTime = "{labels.crawling_info_WebFsCrawlEndTime}";

    /** The key of the message: Indexing exec time (Web/File system) */
    public static final String LABELS_crawling_info_WebFsIndexExecTime = "{labels.crawling_info_WebFsIndexExecTime}";

    /** The key of the message: Index size (Web/File system) */

        if (logger.isDebugEnabled()) {
            logger.debug("Text: {}", text);
        }
        textBuf.append(text);
    }

    protected String parseFilterPaths(final String text, final boolean web, final boolean file) {
        return split(text, "\n")
                .get(stream -> stream.map(String::trim).filter(StringUtil::isNotBlank).map(this::getFilterPath).filter(s -> {
                    if (StringUtil.isBlank(s)) {

import okhttp3.Headers.Companion.headersOf
import okhttp3.WebSocketListener
import okhttp3.internal.addHeaderLenient
import okhttp3.internal.http2.Settings
import okio.Buffer

/** A scripted response to be replayed by the mock web server. */
@ExperimentalOkHttpApi
class MockResponse {
  /** Returns the HTTP response line, such as "HTTP/1.1 200 OK". */
  val status: String

  val code: Int
    get() {

### Concurrence + Parallélisme : Web + Machine Learning

Avec **FastAPI** vous pouvez bénéficier de la concurrence qui est très courante en développement web (c'est l'attrait principal de NodeJS).