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")

        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

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. 🚨

 */
package org.apache.maven.cling.transfer;

import org.apache.maven.api.services.MessageBuilder;

/**
 * Formats file size with the associated <a href="https://en.wikipedia.org/wiki/Metric_prefix">SI</a> prefix
 * (GB, MB, kB) and using the patterns <code>#0.0</code> for numbers between 1 and 10
 * and <code>###0</code> for numbers between 10 and 1000+ by default.
 *

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. 🚨

多个进程通常**不共享任何内存**。 这意味着每个正在运行的进程都有自己的东西、变量和内存。 如果您的代码消耗了大量内存，**每个进程**将消耗等量的内存。

### 服务器内存

例如，如果您的代码加载 **1 GB 大小**的机器学习模型，则当您使用 API 运行一个进程时，它将至少消耗 1 GB RAM。 如果您启动 **4 个进程**（4 个工作进程），每个进程将消耗 1 GB RAM。 因此，您的 API 总共将消耗 **4 GB RAM**。

如果您的远程服务器或虚拟机只有 3 GB RAM，尝试加载超过 4 GB RAM 将导致问题。 🚨


### 多进程 - 一个例子

在此示例中，有一个 **Manager Process** 启动并控制两个 **Worker Processes**。

    private static final String ANSI_DARK_RESET = ESC + "[0m";

    // CHECKSTYLE_OFF: LineLength
    /**
     * Formats file size with the associated <a href="https://en.wikipedia.org/wiki/Metric_prefix">SI</a> prefix
     * (GB, MB, kB) and using the patterns <code>#0.0</code> for numbers between 1 and 10
     * and <code>###0</code> for numbers between 10 and 1000+ by default.
     *

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. 🚨

 */
package org.apache.maven.cli.transfer;

import org.apache.maven.api.services.MessageBuilder;

/**
 * Formats file size with the associated <a href="https://en.wikipedia.org/wiki/Metric_prefix">SI</a> prefix
 * (GB, MB, kB) and using the patterns <code>#0.0</code> for numbers between 1 and 10
 * and <code>###0</code> for numbers between 10 and 1000+ by default.
 *