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
            };

        assertEquals("/path/", item.getPath());
        assertEquals("", item.getEncodedId());
    }

    public void test_largeFileSize() {
        final long largeSize = 10737418240L; // 10 GB
        final StorageItem item = new StorageItem("large-file.zip", "/backups/", false, largeSize, null, "bGFyZ2UtZmlsZS56aXA=");

        assertEquals(largeSize, item.getSize());
    }

    public void test_rootPath() {

Europe/Sofia Europe/Stockholm Europe/Tallinn Europe/Tirane Europe/Tiraspol Europe/Ulyanovsk Europe/Uzhgorod Europe/Vaduz Europe/Vatican Europe/Vienna Europe/Vilnius Europe/Volgograd Europe/Warsaw Europe/Zagreb Europe/Zaporozhye Europe/Zurich Factory GB GB-Eire GMT GMT+0 GMT-0 GMT0 Greenwich HST Hongkong Iceland Indian/Antananarivo Indian/Chagos Indian/Christmas Indian/Cocos Indian/Comoro Indian/Kerguelen Indian/Mahe Indian/Maldives Indian/Mauritius Indian/Mayotte Indian/Reunion Iran Israel Jamaica Japan...

        } else if (size.divide(ONE_GB_BI).compareTo(BigInteger.ZERO) > 0) {
            displaySize = new BigDecimal(size.divide(ONE_MB_BI)).divide(BigDecimal.valueOf(1000)) + "GB";
        } else if (size.divide(ONE_MB_BI).compareTo(BigInteger.ZERO) > 0) {
            displaySize = new BigDecimal(size.divide(ONE_KB_BI)).divide(BigDecimal.valueOf(1000)) + "MB";

                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());

  exit 1
fi

# "TFCI_MACOS_BAZEL_TEST_DIR_PATH" specifies the directory that Bazel should use
# when running tests. Each test will be executed in a separate subdirectory
# inside this directory. TF Mac builds need ~150 GB of disk space to be able to
# run all the tests. Since TFCI Mac VMs execute Bazel test commands in a
# partition with insufficient storage, we specify the
# 'TFCI_MACOS_BAZEL_TEST_DIR_PATH' environment variable to point to a partition

        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()