Authenticating as an end user

This topic explains how to access Google Cloud APIs on behalf of an end user. For general information about authenticating to Google Cloud APIs, see Authentication overview.

Authentication flow

When an application needs to access Google Cloud APIs on behalf of an end user, the application initiates an OAuth consent flow. After the user completes the flow, your application receives the user's credentials. With the credentials, your application can call Google Cloud APIs on behalf of the user.

This process is a protocol called OAuth 2.0.

OAuth 2.0 flow
Your app
User consent
User data

To learn more about OAuth 2.0, see OAuth 2.0.

Specifying OAuth scopes

When authenticating as an end user, you must specify OAuth scopes explicitly. OAuth scopes limit the actions your application can perform on behalf of the end user. For example, these actions might include reading files from Cloud Storage, or managing Google Cloud projects.

See the specific API page for more information on what OAuth scopes are available. For example, if you plan to use the disks.get() method for the Compute Engine API, you would need to set one of these OAuth scopes. Set the minimum scope needed based on your use case.

Granting and limiting access to project resources

If you're using end user credentials to access resources within your project, you must grant the user access to resources within your project. Do this in Google Cloud by setting a role in Identity and Access Management (IAM).

You may want to limit which resources the user has access to. This is especially true when you're allowing the user to access resources in a project that you own. Set roles according to the least privilege the user needs.

Each service has a set of IAM roles, and you can choose to create custom roles instead. For more information, see understanding roles and creating and managing custom roles.

End user authentication example

Complete the following sections to obtain credentials for an end user. The following steps use the BigQuery API, but you can replicate this process with any Google Cloud API that has a client library.

Setting up your project

  1. Sign in to your Google Account.

    If you don't already have one, sign up for a new account.

  2. In the Cloud Console, on the project selector page, select or create a Cloud project.

    Go to the project selector page

  3. Make sure that billing is enabled for your Google Cloud project. Learn how to confirm billing is enabled for your project.

  4. Enable the BigQuery API.

    Enable the API

  5. Install the BigQuery client libraries.
  6. If using Python or Node.js, you must install an additional auth library.

    Python

    Install the oauthlib integration for Google Auth.

    pip install --upgrade google-auth-oauthlib

Creating your client credentials

Create your client credentials in Google Cloud Console.

  1. Go to the OAuth consent screen page in Cloud Console.

  2. On the Credentials page, select the Create credentials button, then select OAuth client ID.

  3. Select Other, then select the Create button. Select the OK button after the success dialogue appears.

  4. Download the credentials by selecting the Download JSON button for the client ID.

    Download JSON button

  5. Save the credentials file to client_secrets.json. This file must be distributed with your application.

Authenticating and calling the API

  1. Use the client credentials to perform the OAuth 2.0 flow.

    Python

    from google_auth_oauthlib import flow
    
    # TODO: Uncomment the line below to set the `launch_browser` variable.
    # launch_browser = True
    #
    # The `launch_browser` boolean variable indicates if a local server is used
    # as the callback URL in the auth flow. A value of `True` is recommended,
    # but a local server does not work if accessing the application remotely,
    # such as over SSH or from a remote Jupyter notebook.
    
    appflow = flow.InstalledAppFlow.from_client_secrets_file(
        "client_secrets.json", scopes=["https://www.googleapis.com/auth/bigquery"]
    )
    
    if launch_browser:
        appflow.run_local_server()
    else:
        appflow.run_console()
    
    credentials = appflow.credentials

  2. Use the authenticated credentials to connect to the BigQuery API.

    Python

    from google.cloud import bigquery
    
    # TODO: Uncomment the line below to set the `project` variable.
    # project = 'user-project-id'
    #
    # The `project` variable defines the project to be billed for query
    # processing. The user must have the bigquery.jobs.create permission on
    # this project to run a query. See:
    # https://cloud.google.com/bigquery/docs/access-control#permissions
    
    client = bigquery.Client(project=project, credentials=credentials)
    
    query_string = """SELECT name, SUM(number) as total
    FROM `bigquery-public-data.usa_names.usa_1910_current`
    WHERE name = 'William'
    GROUP BY name;
    """
    query_job = client.query(query_string)
    
    # Print the results.
    for row in query_job.result():  # Wait for the job to complete.
        print("{}: {}".format(row["name"], row["total"]))

When you run the sample code, the code launches a browser requesting access to the project associated with the client secrets. The resulting credentials can then be used to access the user's BigQuery resources, because the sample requested the BigQuery scope.

In a different use case, you may wish to grant IAM roles to determine what the user can access.

What's next