(using-jupyterhub-rest-api)=

# Using JupyterHub's REST API

This section will give you information on:

- What you can do with the API
- How to create an API token
- Assigning permissions to a token
- Updating to admin services
- Making an API request programmatically using the requests library
- Paginating API requests
- Enabling users to spawn multiple named-servers via the API
- Learn more about JupyterHub's API

Before we discuss about JupyterHub's REST API, you can learn about [REST APIs here](https://en.wikipedia.org/wiki/Representational_state_transfer). A REST
API provides a standard way for users to get and send information to the
Hub.

## What you can do with the API

Using the [JupyterHub REST API](jupyterhub-rest-API), you can perform actions on the Hub,
such as:

- Checking which users are active
- Adding or removing users
- Stopping or starting single user notebook servers
- Authenticating services
- Communicating with an individual Jupyter server's REST API

## Create an API token

To send requests using the JupyterHub API, you must pass an API token with
the request.

While JupyterHub is running, any JupyterHub user can request a token via the `token` page.
This is accessible via a `token` link in the top nav bar from the JupyterHub home page,
or at the URL `/hub/token`.

:::{figure-md}

![token request page](../images/token-page.png)

JupyterHub's API token page
:::

:::{figure-md}
![token-request-success](../images/token-request-success.png)

JupyterHub's token page after successfully requesting a token.

:::

### Register API tokens via configuration

Sometimes, you'll want to pre-generate a token for access to JupyterHub,
typically for use by external services,
so that both JupyterHub and the service have access to the same value.

First, you need to generate a good random secret.
A good way of generating an API token is by running:

```bash
openssl rand -hex 32
```

This `openssl` command generates a random token that can be added to the JupyterHub configuration in `jupyterhub_config.py`.

For external services, this would be registered with JupyterHub via configuration:

```python
c.JupyterHub.services = [
    {
        "name": "my-service",
        "api_token": the_secret_value,
    },
]
```

At this point, requests authenticated with the token will be associated with The service `my-service`.

```{note}
You can also load additional tokens for users via the `JupyterHub.api_tokens` configuration.

However, this option has been deprecated since the introduction of services.
```

## Assigning permissions to a token

Prior to JupyterHub 2.0, there were two levels of permissions:

1. user, and
2. admin

where a token would always have full permissions to do whatever its owner could do.

In JupyterHub 2.0,
specific permissions are now defined as '**scopes**',
and can be assigned both at the user/service level,
and at the individual token level.

This allows e.g. a user with full admin permissions to request a token with limited permissions.

## Updating to admin services

```{note}
The `api_tokens` configuration has been softly deprecated since the introduction of services.
We have no plans to remove it,
but deployments are encouraged to use service configuration instead.
```

If you have been using `api_tokens` to create an admin user
and the token for that user to perform some automations, then
the services' mechanism may be a better fit if you have the following configuration:

```python
c.JupyterHub.admin_users = {"service-admin"}
c.JupyterHub.api_tokens = {
    "secret-token": "service-admin",
}
```

This can be updated to create a service, with the following configuration:

```python
c.JupyterHub.services = [
    {
        # give the token a name
        "name": "service-admin",
        "api_token": "secret-token",
        # "admin": True, # if using JupyterHub 1.x
    },
]

# roles were introduced in JupyterHub 2.0
# prior to 2.0, only "admin": True or False was available

c.JupyterHub.load_roles = [
    {
        "name": "service-role",
        "scopes": [
            # specify the permissions the token should have
            "admin:users",
        ],
        "services": [
            # assign the service the above permissions
            "service-admin",
        ],
    }
]
```

The token will have the permissions listed in the role
(see [scopes][] for a list of available permissions),
but there will no longer be a user account created to house it.
The main noticeable difference between a user and a service is that there will be no notebook server associated with the account
and the service will not show up in the various user list pages and APIs.

## Make an API request

To authenticate your requests, pass the API token in the request's
Authorization header.

### Use requests

Using the popular Python [requests](https://docs.python-requests.org)
library, an API GET request is made, and the request sends an API token for
authorization. The response contains information about the users, here's example code to make an API request for the users of a JupyterHub deployment

```python
import requests

api_url = 'http://127.0.0.1:8081/hub/api'

r = requests.get(api_url + '/users',
    headers={
        'Authorization': f'token {token}',
    }
)

r.raise_for_status()
users = r.json()
```

This example provides a slightly more complicated request, yet the
process is very similar:

```python
import requests

api_url = 'http://127.0.0.1:8081/hub/api'

data = {'name': 'mygroup', 'users': ['user1', 'user2']}

r = requests.post(api_url + '/groups/formgrade-data301/users',
    headers={
        'Authorization': f'token {token}',
    },
    json=data,
)
r.raise_for_status()
r.json()
```

The same API token can also authorize access to the [Jupyter Notebook REST API][]

provided by notebook servers managed by JupyterHub if it has the necessary `access:servers` scope.

(api-pagination)=

## Paginating API requests

```{versionadded} 2.0

```

Pagination is available through the `offset` and `limit` query parameters on
list endpoints, which can be used to return ideally sized windows of results.
Here's example code demonstrating pagination on the `GET /users`
endpoint to fetch the first 20 records.

```python
import os
import requests

api_url = 'http://127.0.0.1:8081/hub/api'

r = requests.get(
    api_url + '/users?offset=0&limit=20',
    headers={
        "Accept": "application/jupyterhub-pagination+json",
        "Authorization": f"token {token}",
    },
)
r.raise_for_status()
r.json()
```

For backward-compatibility, the default structure of list responses is unchanged.
However, this lacks pagination information (e.g. is there a next page),
so if you have enough users that they won't fit in the first response,
it is a good idea to opt-in to the new paginated list format.
There is a new schema for list responses which include pagination information.
You can request this by including the header:

```
Accept: application/jupyterhub-pagination+json
```

with your request, in which case a response will look like:

```python
{
  "items": [
    {
      "name": "username",
      "kind": "user",
      ...
    },
  ],
  "_pagination": {
    "offset": 0,
    "limit": 20,
    "total": 50,
    "next": {
      "offset": 20,
      "limit": 20,
      "url": "http://127.0.0.1:8081/hub/api/users?limit=20&offset=20"
    }
  }
}
```

where the list results (same as pre-2.0) will be in `items`,
and pagination info will be in `_pagination`.
The `next` field will include the `offset`, `limit`, and `url` for requesting the next page.
`next` will be `null` if there is no next page.

Pagination is governed by two configuration options:

- `JupyterHub.api_page_default_limit` - the page size, if `limit` is unspecified in the request
  and the new pagination API is requested
  (default: 50)
- `JupyterHub.api_page_max_limit` - the maximum page size a request can ask for (default: 200)

Pagination is enabled on the `GET /users`, `GET /groups`, and `GET /proxy` REST endpoints.

## Enabling users to spawn multiple named-servers via the API

Support for multiple servers per user was introduced in JupyterHub [version 0.8.](changelog)
Prior to that, each user could only launch a single default server via the API
like this:

```bash
curl -X POST -H "Authorization: token <token>" "http://127.0.0.1:8081/hub/api/users/<user>/server"
```

With the named-server functionality, it's now possible to launch more than one
specifically named servers against a given user. This could be used, for instance,
to launch each server based on a different image.

First you must enable named-servers by including the following setting in the `jupyterhub_config.py` file.

`c.JupyterHub.allow_named_servers = True`

If you are using the [zero-to-jupyterhub-k8s](https://github.com/jupyterhub/zero-to-jupyterhub-k8s) set-up to run JupyterHub,
then instead of editing the `jupyterhub_config.py` file directly, you could pass
the following as part of the `config.yaml` file, as per the [tutorial](https://z2jh.jupyter.org/en/latest/):

```bash
hub:
  extraConfig: |
    c.JupyterHub.allow_named_servers = True
```

With that setting in place, a new named-server is activated like this:

```bash
curl -X POST -H "Authorization: token <token>" "http://127.0.0.1:8081/hub/api/users/<user>/servers/<serverA>"
curl -X POST -H "Authorization: token <token>" "http://127.0.0.1:8081/hub/api/users/<user>/servers/<serverB>"
```

The same servers can be stopped by substituting `DELETE` for `POST` above.

### Some caveats for using named-servers

For named-servers via the API to work, the spawner used to spawn these servers
will need to be able to handle the case of multiple servers per user and ensure
uniqueness of names, particularly if servers are spawned via docker containers
or kubernetes pods.

## Learn more about the API

You can see the full [JupyterHub REST API](jupyterhub-rest-api) for more details.

[openapi initiative]: https://www.openapis.org/
[jupyterhub rest api]: ./rest-api
[scopes]: ../rbac/scopes.md
[jupyter notebook rest api]: https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/jupyter/notebook/HEAD/notebook/services/api/api.yaml