Por exemplo, se seu código carrega um modelo de Machine Learning com **1 GB de tamanho**, quando você executa um processo com sua API, ele consumirá pelo menos 1 GB de RAM. E se você iniciar **4 processos** (4 trabalhadores), cada um consumirá 1 GB de RAM. Então, no total, sua API consumirá **4 GB de RAM**.

E se o seu servidor remoto ou máquina virtual tiver apenas 3 GB de RAM, tentar carregar mais de 4 GB de RAM causará problemas. 🚨

Por ejemplo, si tu código carga un modelo de Machine Learning con **1 GB de tamaño**, cuando ejecutas un proceso con tu API, consumirá al menos 1 GB de RAM. Y si inicias **4 procesos** (4 workers), cada uno consumirá 1 GB de RAM. Así que, en total, tu API consumirá **4 GB de RAM**.

Y si tu servidor remoto o máquina virtual solo tiene 3 GB de RAM, intentar cargar más de 4 GB de RAM causará problemas. 🚨

### Múltiples Procesos - Un Ejemplo

Wenn Ihr Code beispielsweise ein Machine-Learning-Modell mit **1 GB Größe** lädt und Sie einen Prozess mit Ihrer API ausführen, verbraucht dieser mindestens 1 GB RAM. Und wenn Sie **4 Prozesse** (4 Worker) starten, verbraucht jeder 1 GB RAM. Insgesamt verbraucht Ihre API also **4 GB RAM**.

Und wenn Ihr entfernter Server oder Ihre virtuelle Maschine nur über 3 GB RAM verfügt, führt der Versuch, mehr als 4 GB RAM zu laden, zu Problemen. 🚨

For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.

And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨

        void shouldHandleTypicalFileSystemSizes() {
            // Test common file system sizes
            long[] typicalSizes = { 1024L * 1024L * 1024L, // 1 GB
                    1024L * 1024L * 1024L * 10L, // 10 GB
                    1024L * 1024L * 1024L * 100L, // 100 GB
                    1024L * 1024L * 1024L * 1024L, // 1 TB
                    1024L * 1024L * 1024L * 1024L * 10L // 10 TB
            };

                return "lang";
            }
        };
        ComponentUtil.setFessConfig(mockConfig);

        // Setup mock request manager
        RequestManager mockRequestManager = createMockRequestManager("en-GB");

        // Execute
        OptionalThing<Locale> result = provider.findBusinessLocale(null, mockRequestManager);

        // Verify
        assertTrue(result.isPresent());

        assertFalse(labelTypeHelper.matchLocale(Locale.US, Locale.UK));

        // Test with same language but different country
        Locale enUS = new Locale("en", "US");
        Locale enGB = new Locale("en", "GB");
        assertFalse(labelTypeHelper.matchLocale(enUS, enGB));

        // Test with same language, target has no country
        Locale en = new Locale("en");
        assertTrue(labelTypeHelper.matchLocale(enUS, en));

    val connection = server.url("/").toUrl().openConnection() as HttpURLConnection
    connection.setRequestMethod("POST")
    connection.setDoOutput(true)
    connection.setFixedLengthStreamingMode(1024 * 1024 * 1024) // 1 GB
    connection.connect()
    val out = connection.outputStream
    val data = ByteArray(1024 * 1024)
    var i = 0
    while (i < 1024) {
      try {
        out.write(data)
        out.flush()

    val connection = server.url("/").toUrl().openConnection() as HttpURLConnection
    connection.requestMethod = "POST"
    connection.doOutput = true
    connection.setFixedLengthStreamingMode(1024 * 1024 * 1024) // 1 GB
    connection.connect()
    val out = connection.outputStream
    val data = ByteArray(1024 * 1024)
    var i = 0
    while (i < 1024) {
      try {
        out!!.write(data)
        out.flush()