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from __future__ import annotations

import csv
from operator import attrgetter
from tempfile import NamedTemporaryFile
from typing import TYPE_CHECKING, Sequence

from airflow.models import BaseOperator
from import GoogleAdsHook
from import GCSHook

    from airflow.utils.context import Context

[docs]class GoogleAdsToGcsOperator(BaseOperator): """Fetch 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: .. 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 :param query: Google Ads Query Language API query :param attributes: List of Google Ads Row attributes to extract :param bucket: The GCS bucket to upload to :param obj: GCS path to save the object. Must be the full file path (ex. `path/to/file.txt`) :param gcp_conn_id: Airflow Google Cloud connection ID :param google_ads_conn_id: Airflow Google Ads connection ID :param page_size: The number of results per API page request. Max 10,000 :param gzip: Option to compress local file or file data for upload :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). :param api_version: Optional Google Ads API version to use. """
[docs] template_fields: Sequence[str] = ( "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: str | Sequence[str] | None = None, api_version: str | None = 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: Context) -> 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 =, 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,, gzip=self.gzip, )"%s uploaded to GCS", self.obj)

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