assertEquals("1.0 GB", format.format(1000L * 1000L * 1000L));
        assertEquals("5.5 GB", format.format(5500L * 1000L * 1000L));
        assertEquals("10 GB", format.format(10L * 1000L * 1000L * 1000L));
        assertEquals("15 GB", format.format(15L * 1000L * 1000L * 1000L));
        assertEquals("1000 GB", format.format(1000L * 1000L * 1000L * 1000L));
    }

    @Test

                Arguments.of(1000L * 1000L * 1000L, "1.0 GB"),
                Arguments.of(5500L * 1000L * 1000L, "5.5 GB"),
                Arguments.of(10L * 1000L * 1000L * 1000L, "10 GB"),
                Arguments.of(15L * 1000L * 1000L * 1000L, "15 GB"),
                Arguments.of(1000L * 1000L * 1000L * 1000L, "1000 GB"));
    }

    @ParameterizedTest
    @MethodSource("sizeTestData")

而多個行程通常「不共享記憶體」。這表示每個執行中的行程都有自己的東西、變數與記憶體。如果你的程式碼會用掉大量記憶體，「每個行程」都會消耗等量的記憶體。

### 伺服器記憶體 { #server-memory }

例如，如果你的程式碼載入一個「1 GB 大小」的機器學習模型，當你用一個行程執行你的 API，它就會至少吃掉 1 GB 的 RAM。若你啟動「4 個行程」（4 個 workers），每個會吃 1 GB RAM。總計你的 API 會吃掉「4 GB RAM」。

如果你的遠端伺服器或虛擬機只有 3 GB RAM，嘗試載入超過 4 GB 的 RAM 就會出問題。🚨

### 多個行程 - 範例 { #multiple-processes-an-example }

在這個例子中，有一個「管理行程（Manager Process）」會啟動並控制兩個「工作行程（Worker Processes）」。

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()

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. 🚨