*.hprof

# Work dirs
# ---------
/incoming-distributions
/intTestHomeDir

# Logs
# ----
/*.log

# Thread dumps for troubleshooting
*.threaddump
# ps output for cleaning up leaking Java processes
*.psoutput

# oh-my-zsh gradle plugin
.gradletasknamecache

# Added GE support maven support to the maven build in .teamcity, per the GE docs, this dir is NOT to be committed
.teamcity/.mvn/.develocity/

## Репликация — процессы и память { #replication-processes-and-memory }

В приложении FastAPI, используя серверную программу (например, команду `fastapi`, которая запускает Uvicorn), запуск в **одном процессе** уже позволяет обслуживать нескольких клиентов одновременно.

Но во многих случаях вы захотите одновременно запустить несколько процессов‑воркеров.

### Несколько процессов — Воркеры { #multiple-processes-workers }

## Реплікація - процеси та пам'ять { #replication-processes-and-memory }

У застосунку FastAPI, використовуючи серверну програму, як-от команду `fastapi`, що запускає Uvicorn, один запуск в **одному процесі** може обслуговувати кількох клієнтів рівночасно.

Але часто ви захочете запускати кілька процесів-працівників одночасно.

### Кілька процесів - працівники { #multiple-processes-workers }

import org.codelibs.fess.Constants;
import org.codelibs.fess.exception.QueryParseException;
import org.lastaflute.web.util.LaRequestUtil;

import jakarta.annotation.PostConstruct;

/**
 * A query parser that processes search queries and converts them to Lucene Query objects.
 * This class provides a flexible architecture using a chain of filters to process and transform
 * queries before they are parsed by the underlying Lucene query parser.
 *

            ((HttpsURLConnection) connection).setSSLSocketFactory(sslSocketFactory);
        }
        return connection;
    }

    /**
     * Executes the request and processes the response.
     *
     * @param actionListener the action listener for handling the response
     * @param exceptionListener the exception listener for handling exceptions
     */

Mais gardez à l'esprit que, comme tout est géré en mémoire, dans une seule liste, cela ne fonctionnera que tant que le processus s'exécute et uniquement avec un seul processus.

Si vous avez besoin de quelque chose de facile à intégrer avec FastAPI mais plus robuste, pris en charge par Redis, PostgreSQL ou autres, consultez [encode/broadcaster](https://github.com/encode/broadcaster).

     * @param request The HTTP servlet request.
     * @return True if the request matches, false otherwise.
     */
    boolean matches(HttpServletRequest request);

    /**
     * Processes the request through this API manager.
     * @param request The HTTP servlet request.
     * @param response The HTTP servlet response.
     * @param chain The filter chain.

        final Builder builder = Settings.builder()
                .putList("http.hosts", hosts)
                .put("processors", fessConfig.getCrawlerHttpProcessors())
                .put("http.heartbeat_interval", fessConfig.getFesenHeartbeatInterval());
        final String username = fessConfig.getFesenUsername();

    protected AccessResult<?> createAccessResult(final ResponseData responseData, final ResultData resultData) {
        return super.createAccessResult(responseData, ingest(responseData, resultData));
    }

    /**
     * Processes the result data through all available ingesters.
     * Each ingester can transform and enrich the data before it is indexed.
     *
     * @param responseData the response data from the crawled resource

///

---

In this scenario of the parallel burgers, you are a computer / program 🤖 with two processors (you and your crush), both waiting 🕙 and dedicating their attention ⏯ to be "waiting on the counter" 🕙 for a long time.

The fast food store has 8 processors (cashiers/cooks). While the concurrent burgers store might have had only 2 (one cashier and one cook).