## Requirements and limits

- ListObjectsV2 can only list the most recent ZIP archive version of your object, applicable only for versioned buckets.
- ListObjectsV2 API calls must be used to list zip file content.
- Range requests for GetObject/HeadObject for individual files from zip is not supported.

. . .
```

Refer [Prometheus documentation](https://prometheus.io/docs/introduction/first_steps/) for more details.

### 2. Configure authentication type for Prometheus metrics

MinIO supports two authentication modes for Prometheus either `jwt` or `public`, by default MinIO runs in `jwt` mode. To allow public access without authentication for prometheus metrics set environment as follows.

```
export MINIO_PROMETHEUS_AUTH_TYPE="public"

/// info

A "bearer" token is not the only option.

But it's the best one for our use case.

And it might be the best for most use cases, unless you are an OAuth2 expert and know exactly why there's another option that better suits your needs.

In that case, **FastAPI** also provides you with the tools to build it.

///

See our web documentation on [Deploying MinIO in Standalone Mode](Deploy Standalone MinIO in a Container) for a more structured tutorial on deploying MinIO in a container.

## Prerequisites

Docker installed on your machine. Download the relevant installer from [here](https://www.docker.com/community-edition#/download).

Créez une variable `ALGORITHM` avec l'algorithme utilisé pour signer le jeton JWT, et définissez-la à `"HS256"`.

Créez une variable pour l'expiration du jeton.

Définissez un modèle Pydantic qui sera utilisé dans le point de terminaison du jeton pour la réponse.

Créez une fonction utilitaire pour générer un nouveau jeton d'accès.

### Server Memory { #server-memory }

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. 🚨

You would have **one single `engine` object** for all your code to connect to the same database.

{* ../../docs_src/sql_databases/tutorial001_an_py310.py ln[14:18] hl[14:15,17:18] *}

Using `check_same_thread=False` allows FastAPI to use the same SQLite database in different threads. This is necessary as **one single request** could use **more than one thread** (for example in dependencies).

    # o extendedDBFluteInitializerClass: (NotRequired - Default null)
    #  If you want to extend the embedded DBFlute initializer,
    #  specify the class name of your original initializer
    #  that extends the embedded one.
    #  *Basically for fixed DBFluteConfig settings
    #
    #; extendedDBFluteInitializerClass = com.example.ExtendedDBFluteInitializer
    # - - - - - - - - - -/

	if err != nil {
		t.Fatal(err)
	}
	defer z.Close()

	for _, test := range []struct {
		name string
		want time.Time
	}{
		{
			"a",
			time.Date(2021, 4, 19, 12, 29, 56, 0, timeZone(-7*time.Hour)).UTC(),
		},
		{
			"a/b/c",
			time.Date(2021, 4, 19, 12, 29, 59, 0, timeZone(-7*time.Hour)).UTC(),
		},
	} {
		fi, err := fs.Stat(z, test.name)
		if err != nil {

This directory contains JLine native libraries extracted from JLine JAR.

You can add your own build for platforms not natively supported by JLine.
See here [1] on how to compile for your platform and here [2] how libraries
follow JLine's directory and filename conventions.

[1] https://github.com/jline/jline3/tree/master/native