Here's a little bit of that history.

## Alternatives

I have been creating APIs with complex requirements for several years (Machine Learning, distributed systems, asynchronous jobs, NoSQL databases, etc), leading several teams of developers.

As part of that, I needed to investigate, test and use many alternatives.

### Create a backup job

#### Backup Virtual Machines with Veeam Backup and Replication

- Under Home > Jobs > Backup in Navigation Pane, click on Backup Job button in the ribbon and choose Virtual Machine. Follow the on screen wizard.

- On the Storage screen, choose the Scale-out Backup Repository that was configured previously.

MinIO uses [`klauspost/compress/s2`](https://github.com/klauspost/compress/tree/master/s2)
streaming compression due to its stability and performance.

This algorithm is specifically optimized for machine generated content.
Write throughput is typically at least 500MB/s per CPU core,
and scales with the number of available CPU cores.
Decompression speed is typically at least 1GB/s.

	app := cli.NewApp()
	app.Name = name
	app.Author = "MinIO, Inc."
	app.Version = ReleaseTag
	app.Usage = "High Performance Object Storage"
	app.Description = `Build high performance data infrastructure for machine learning, analytics and application data workloads with MinIO`
	app.Flags = GlobalFlags
	app.HideHelpCommand = true // Hide `help, h` command, we already have `minio --help`.
	app.Commands = commands

</p>

<p>
Reads of memory locations larger than a single machine word
are encouraged but not required to meet the same semantics
as word-sized memory locations,
observing a single allowed write <i>w</i>.
For performance reasons,
implementations may instead treat larger operations
as a set of individual machine-word-sized operations
in an unspecified order.

build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1

# Prevent double-compilation of some TF code, ref. b/183279666 (internal)
# > TF's gen_api_init_files has a genrule to run the core TensorFlow code
# > on the host machine. If we don't have --distinct_host_configuration=false,
# > the core TensorFlow code will be built once for the host and once for the
# > target platform.

In the code, a `Bucket` represents the main entrypoint of communication with the database.

This utility function will:

* Connect to a **Couchbase** cluster (that might be a single machine).
    * Set defaults for timeouts.
* Authenticate in the cluster.
* Get a `Bucket` instance.
    * Set defaults for timeouts.
* Return it.

```Python hl_lines="12-21"

## 服务器主机和服务器程序

关于名称，有一个小细节需要记住。 💡

“**服务器**”一词通常用于指远程/云计算机（物理机或虚拟机）以及在该计算机上运行的程序（例如 Uvicorn）。

请记住，当您一般读到“服务器”这个名词时，它可能指的是这两者之一。

当提到远程主机时，通常将其称为**服务器**，但也称为**机器**(machine)、**VM**（虚拟机）、**节点**。 这些都是指某种类型的远程计算机，通常运行 Linux，您可以在其中运行程序。


## 安装服务器程序

您可以使用以下命令安装 ASGI 兼容服务器：

=== "Uvicorn"

  }

  companion object {
    /**
     * A network that resolves only one IP address per host. Use this when testing route selection
     * fallbacks to prevent the host machine's various IP addresses from interfering.
     */
    private val SINGLE_INET_ADDRESS_DNS =
      Dns { hostname ->
        val addresses = Dns.SYSTEM.lookup(hostname)
        listOf(addresses[0])
      }

En utilisant le mode Docker Swarm, vous pouvez commencer par un "cluster" d'une seule machine (il peut même s'agir
d'un serveur à 5 USD/mois) et ensuite vous pouvez vous développer autant que vous le souhaitez en ajoutant d'autres serveurs.