final FileConfig fileConfig = getFileConfig(body).map(entity -> {
            try {
                fileConfigService.store(entity);
            } catch (final Exception e) {
                logger.warn("Failed to process a request.", e);
                throwValidationErrorApi(messages -> messages.addErrorsCrudFailedToCreateCrudTable(GLOBAL, buildThrowableMessage(e)));
            }
            return entity;
        }).orElseGet(() -> {

import org.codelibs.fess.util.ComponentUtil;
import org.lastaflute.web.Execute;
import org.lastaflute.web.response.ActionResponse;
import org.lastaflute.web.response.HtmlResponse;
import org.lastaflute.web.ruts.process.ActionRuntime;

/**
 * Admin action for Search Request.
 *
 */
public class AdminSereqAction extends FessAdminAction {

    /**
     * Default constructor.
     */
    public AdminSereqAction() {

)

// bucketMeasurement captures the bandwidth details for one bucket
type bucketMeasurement struct {
	lock                 sync.Mutex
	bytesSinceLastWindow uint64    // Total bytes since last window was processed
	startTime            time.Time // Start time for window
	expMovingAvg         float64   // Previously calculate sliding window
}

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`).

This is the basic idea. But you will probably want to take care of some additional things, like:

* Security - HTTPS
* Running on startup
* Restarts
* Replication (the number of processes running)
* Memory
* Previous steps before starting

        try {
            this.threadPool = null;
            final RequestProcessor processor = new RequestProcessor(encoding, threshold);
            connect(processor, e -> {
                throw new CurlException("Failed to process a request.", e);
            });
            return processor.getResponse();
        } finally {
            this.threadPool = originalThreadPool;
        }
    }

Wenn das Programm nun Dinge in den Arbeitsspeicher lädt, zum Beispiel ein Modell für maschinelles Lernen in einer Variablen oder den Inhalt einer großen Datei in einer Variablen, verbraucht das alles **einen Teil des Arbeitsspeichers (RAM – Random Access Memory)** des Servers.

 * <p>
 * Model transformers allow plugins and extensions to modify the POM model during the build process.
 * Transformations can be applied at three different stages:
 * <ol>
 *   <li>File model - The raw model as read directly from the file</li>
 *   <li>Raw model - The model after inheritance has been applied</li>
 *   <li>Effective model - The fully processed model with all interpolation and inheritance applied</li>
 * </ol>
 * <p>

Y también después de que se genere la `response`, antes de devolverla.

Por ejemplo, podrías añadir un custom header `X-Process-Time` que contenga el tiempo en segundos que tomó procesar la request y generar una response:

{* ../../docs_src/middleware/tutorial001_py39.py hl[10,12:13] *}

/// tip | Consejo

                }
            }
        }
    }

    /**
     * Processes requests and applies character encoding conversion if needed.
     * Checks if the request path matches any configured encoding rule and
     * performs URL redirection with proper parameter encoding.
     *
     * @param request the servlet request to process
     * @param response the servlet response to use for redirection

import org.dbflute.optional.OptionalThing;
import org.lastaflute.core.message.MessageManager;
import org.lastaflute.web.login.LoginManager;
import org.lastaflute.web.response.ActionResponse;
import org.lastaflute.web.ruts.process.ActionRuntime;
import org.lastaflute.web.validation.VaMessenger;

import jakarta.annotation.Resource;
import jakarta.servlet.http.HttpServletRequest;

/**