* <strong>Performance</strong>).
 *
 * <p><strong>Cycle Detection</strong>
 *
 * <p>Deadlocks can arise when locks are acquired in an order that forms a cycle. In a simple
 * example involving two locks and two threads, deadlock occurs when one thread acquires Lock A, and
 * then Lock B, while another thread acquires Lock B, and then Lock A:
 *
 * <pre>

Pero también puedes explotar los beneficios del paralelismo y la multiprocesamiento (tener múltiples procesos ejecutándose en paralelo) para cargas de trabajo **CPU bound** como las de los sistemas de Machine Learning.

Eso, más el simple hecho de que Python es el lenguaje principal para **Data Science**, Machine Learning y especialmente Deep Learning, hacen de FastAPI una muy buena opción para APIs web de Data Science / Machine Learning y aplicaciones (entre muchas otras).

   * serialized with XML 1.0.
   *
   * <p>For SOAP 1.1 messages, see {@code XML_UTF_8} per <a
   * href="http://www.w3.org/TR/2000/NOTE-SOAP-20000508/">W3C Note on Simple Object Access Protocol
   * (SOAP) 1.1</a>
   *
   * @since 20.0
   */
  public static final MediaType SOAP_XML_UTF_8 = createConstantUtf8(APPLICATION_TYPE, "soap+xml");

    url =
      server.url(
        """/api/login?
      |user=test_user&
      |authentication=basic&
      |password=confidential_password&
      |authentication=rather simple login method
        """.trimMargin(),
      )
    val networkInterceptor =
      HttpLoggingInterceptor(networkLogs).setLevel(
        Level.BASIC,
      )

			query:      `SELECT * from s3object s WHERE 'bar' IN s.synonyms[*]`,
			wantResult: `{"id":0,"title":"Test Record","desc":"Some text","synonyms":["foo","bar","whatever"]}`,
		},
		{
			name:  "simple-in-array",
			query: `SELECT * from s3object s WHERE s.id IN (1,3)`,
			wantResult: `{"id":1,"title":"Second Record","desc":"another text","synonyms":["some","synonym","value"]}

     *
     * @param serverAddress the server address
     * @return cluster name or null
     */
    private String getClusterName(InetAddress serverAddress) {
        // Simple implementation - could be enhanced with proper cluster discovery
        String hostname = serverAddress.getHostName();
        if (hostname != null && hostname.contains(".")) {

여러분은 자동으로 생성된 인터랙티브 API(<a href="https://github.com/swagger-api/swagger-ui" class="external-link" target="_blank">Swagger UI</a>에서 제공된)를 볼 수 있습니다:

![Swagger UI](https://fastapi.tiangolo.com/img/index/index-01-swagger-ui-simple.png)

## 대안 API 문서

        * [Microservice in Python using FastAPI](https://dev.to/paurakhsharma/microservice-in-python-using-fastapi-24cc)  by [Paurakh Sharma Humagain](https://x.com/PaurakhSharma).
        * [Build simple API service with Python FastAPI — Part 1](https://dev.to/cuongld2/build-simple-api-service-with-python-fastapi-part-1-581o) by [cuongld2](https://dev.to/cuongld2).

But you can also exploit the benefits of parallelism and multiprocessing (having multiple processes running in parallel) for **CPU bound** workloads like those in Machine Learning systems.

That, plus the simple fact that Python is the main language for **Data Science**, Machine Learning and especially Deep Learning, make FastAPI a very good match for Data Science / Machine Learning web APIs and applications (among many others).

        setSystemProperty(Constants.LDAP_SECURITY_AUTHENTICATION, value);
    }

    default String getLdapSecurityAuthentication() {
        return getSystemProperty(Constants.LDAP_SECURITY_AUTHENTICATION, "simple");
    }

    default void setLdapProviderUrl(final String value) {
        setSystemProperty(Constants.LDAP_PROVIDER_URL, value);
    }

    default String getLdapProviderUrl() {