# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import csv
from operator import attrgetter
from tempfile import NamedTemporaryFile
from typing import List, Optional, Sequence, Union
from airflow.models import BaseOperator
from airflow.providers.google.ads.hooks.ads import GoogleAdsHook
from airflow.providers.google.cloud.hooks.gcs import GCSHook
[docs]class GoogleAdsToGcsOperator(BaseOperator):
"""
Fetches the daily results from the Google Ads API for 1-n clients
Converts and saves the data as a temporary CSV file
Uploads the CSV to Google Cloud Storage
.. seealso::
For more information on the Google Ads API, take a look at the API docs:
https://developers.google.com/google-ads/api/docs/start
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:GoogleAdsToGcsOperator`
:param client_ids: Google Ads client IDs to query
:type client_ids: List[str]
:param query: Google Ads Query Language API query
:type query: str
:param attributes: List of Google Ads Row attributes to extract
:type attributes: List[str]
:param bucket: The GCS bucket to upload to
:type bucket: str
:param obj: GCS path to save the object. Must be the full file path (ex. `path/to/file.txt`)
:type obj: str
:param gcp_conn_id: Airflow Google Cloud connection ID
:type gcp_conn_id: str
:param google_ads_conn_id: Airflow Google Ads connection ID
:type google_ads_conn_id: str
:param page_size: The number of results per API page request. Max 10,000
:type page_size: int
:param gzip: Option to compress local file or file data for upload
:type gzip: bool
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
:type impersonation_chain: Union[str, Sequence[str]]
:param api_version: Optional Google Ads API version to use.
:type api_version: Optional[str]
"""
[docs] template_fields = (
"client_ids",
"query",
"attributes",
"bucket",
"obj",
"impersonation_chain",
)
def __init__(
self,
*,
client_ids: List[str],
query: str,
attributes: List[str],
bucket: str,
obj: str,
gcp_conn_id: str = "google_cloud_default",
google_ads_conn_id: str = "google_ads_default",
page_size: int = 10000,
gzip: bool = False,
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
api_version: Optional[str] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.client_ids = client_ids
self.query = query
self.attributes = attributes
self.bucket = bucket
self.obj = obj
self.gcp_conn_id = gcp_conn_id
self.google_ads_conn_id = google_ads_conn_id
self.page_size = page_size
self.gzip = gzip
self.impersonation_chain = impersonation_chain
self.api_version = api_version
[docs] def execute(self, context: dict) -> None:
service = GoogleAdsHook(
gcp_conn_id=self.gcp_conn_id,
google_ads_conn_id=self.google_ads_conn_id,
api_version=self.api_version,
)
rows = service.search(client_ids=self.client_ids, query=self.query, page_size=self.page_size)
try:
getter = attrgetter(*self.attributes)
converted_rows = [getter(row) for row in rows]
except Exception as e:
self.log.error("An error occurred in converting the Google Ad Rows. \n Error %s", e)
raise
with NamedTemporaryFile("w", suffix=".csv") as csvfile:
writer = csv.writer(csvfile)
writer.writerows(converted_rows)
csvfile.flush()
hook = GCSHook(gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain)
hook.upload(
bucket_name=self.bucket,
object_name=self.obj,
filename=csvfile.name,
gzip=self.gzip,
)
self.log.info("%s uploaded to GCS", self.obj)