Source code for apache_beam.internal.gcp.auth

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"""Dataflow credentials and authentication."""

import datetime
import json
import logging
import os
import urllib2

from oauth2client.client import GoogleCredentials
from oauth2client.client import OAuth2Credentials

from apache_beam.utils import retry

# When we are running in GCE, we can authenticate with VM credentials.
is_running_in_gce = False

# When we are running in GCE, this value is set based on worker startup
# information.
executing_project = None


[docs]def set_running_in_gce(worker_executing_project): """For internal use only; no backwards-compatibility guarantees. Informs the authentication library that we are running in GCE. When we are running in GCE, we have the option of using the VM metadata credentials for authentication to Google services. Args: worker_executing_project: The project running the workflow. This information comes from worker startup information. """ global is_running_in_gce global executing_project is_running_in_gce = True executing_project = worker_executing_project
[docs]class AuthenticationException(retry.PermanentException): pass
class _GCEMetadataCredentials(OAuth2Credentials): """For internal use only; no backwards-compatibility guarantees. Credential object initialized using access token from GCE VM metadata.""" def __init__(self, user_agent=None): """Create an instance of GCEMetadataCredentials. These credentials are generated by contacting the metadata server on a GCE VM instance. Args: user_agent: string, The HTTP User-Agent to provide for this application. """ super(_GCEMetadataCredentials, self).__init__( None, # access_token None, # client_id None, # client_secret None, # refresh_token datetime.datetime(2010, 1, 1), # token_expiry, set to time in past. None, # token_uri user_agent) @retry.with_exponential_backoff( retry_filter=retry.retry_on_server_errors_and_timeout_filter) def _refresh(self, http_request): refresh_time = datetime.datetime.now() metadata_root = os.environ.get( 'GCE_METADATA_ROOT', 'metadata.google.internal') token_url = ('http://{}/computeMetadata/v1/instance/service-accounts/' 'default/token').format(metadata_root) req = urllib2.Request(token_url, headers={'Metadata-Flavor': 'Google'}) token_data = json.loads(urllib2.urlopen(req).read()) self.access_token = token_data['access_token'] self.token_expiry = (refresh_time + datetime.timedelta(seconds=token_data['expires_in']))
[docs]def get_service_credentials(): """For internal use only; no backwards-compatibility guarantees. Get credentials to access Google services.""" user_agent = 'beam-python-sdk/1.0' if is_running_in_gce: # We are currently running as a GCE taskrunner worker. # # TODO(ccy): It's not entirely clear if these credentials are thread-safe. # If so, we can cache these credentials to save the overhead of creating # them again. return _GCEMetadataCredentials(user_agent=user_agent) else: client_scopes = [ 'https://www.googleapis.com/auth/bigquery', 'https://www.googleapis.com/auth/cloud-platform', 'https://www.googleapis.com/auth/devstorage.full_control', 'https://www.googleapis.com/auth/userinfo.email', 'https://www.googleapis.com/auth/datastore' ] try: credentials = GoogleCredentials.get_application_default() credentials = credentials.create_scoped(client_scopes) logging.debug('Connecting using Google Application Default ' 'Credentials.') return credentials except Exception: logging.warning('Unable to find default credentials to use.') raise