using OAuth2 user account authentication in the python google cloud API from jupyter notebook

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I am trying to access BigQuery from python code in Jupyter notebook run on a local machine. So I installed the google cloud API packages on my laptop.

I need to pass the OAuth2 authentication. But unfortunately, I only have user account to our bigquery. I do not have service account and not application credentials, nor do I have the permissions to create such. I am only allowed to work with user account.

When running the bigquery.Client() function, it appears to look for application credentials by looking at an environment variable GOOGLE_APPLICATION_CREDENTIALS. But this, it seems, for my non existing application credentials.

I cannot find any other way to connect using user account authentication. But I find it extremely weird because:

  1. The google API for R language works simply with user authentication. Parallel code in R (it has different API) just works!
  2. I run the code from the dataspell IDE. I have created in the IDE a database resource connection to bigquery (with my user authentication). There I am capable of opening a console for the database and I can run SQL queries in the console with no problem. I have attached the bigquery session to my python notebook, and I can see my notebook attached to the big query session in the services pane. But I am still missing something in order to access some valid running connection in the python code. (I do not know how to get a python object representing a valid connected client).

I have been reading manuals from google and looked for code examples for hours... Alas, I cannot find any description of connecting a client using user account from my notebook.

Please, can someone help?

jupyter-notebook google-bigquery oauth-2.0 google-oauth python
1 Answers

You can use the pydata-google-auth library to authenticate with a user account. This function loads credentials from a cache on disk or initiates an OAuth2.0 flow if the credentials are not found. This is not the recommended method to do an authentication.

import pandas_gbq
import pydata_google_auth

SCOPES = [
    'https://www.googleapis.com/auth/cloud-platform',
    'https://www.googleapis.com/auth/drive',
]

credentials = pydata_google_auth.get_user_credentials(
    SCOPES,
    # Set auth_local_webserver to True to have a slightly more convienient
    # authorization flow. Note, this doesn't work if you're running from a
    # notebook on a remote sever, such as over SSH or with Google Colab.
    auth_local_webserver=True,
)

df = pandas_gbq.read_gbq(
    "SELECT my_col FROM `my_dataset.my_table`",
    project_id='YOUR-PROJECT-ID',
    credentials=credentials,
)

The recommended way to do the authentication is to contact your GCP administrator and tell them to create a key for your account following the next instructions.

Then you can use this code to set up the authentication with the key that you have:

from google.oauth2 import service_account

credentials = service_account.Credentials.from_service_account_file(
    '/path/to/key.json')

You can see more of the documentation here.

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