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"""This module contains Facebook Ad Reporting to GCS operators."""

from __future__ import annotations

import csv
import tempfile
from enum import Enum
from typing import TYPE_CHECKING, Any, Sequence

from airflow.exceptions import AirflowException
from airflow.models import BaseOperator
from import FacebookAdsReportingHook
from import GCSHook

    from facebook_business.adobjects.adsinsights import AdsInsights

    from airflow.utils.context import Context

[docs]class FlushAction(Enum): """Facebook Ads Export Options."""
[docs] EXPORT_ONCE = "ExportAtOnce"
[docs] EXPORT_EVERY_ACCOUNT = "ExportEveryAccount"
[docs]class FacebookAdsReportToGcsOperator(BaseOperator): """Fetch from Facebook Ads API. This converts and saves the data as a temporary JSON file, and uploads the JSON to Google Cloud Storage. .. seealso:: For more information on the Facebook Ads API, take a look at the API docs: .. seealso:: For more information on the Facebook Ads Python SDK, take a look at the docs: .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:FacebookAdsReportToGcsOperator` :param bucket_name: The GCS bucket to upload to :param object_name: 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 facebook_conn_id: Airflow Facebook Ads connection ID :param api_version: The version of Facebook API. Default to None. If it is None, it will use the Facebook business SDK default version. :param fields: List of fields that is obtained from Facebook. Found in AdsInsights.Field class. :param parameters: Parameters that determine the query for Facebook :param gzip: Option to compress local file or file data for upload :param upload_as_account: Option to export file with account_id This parameter only works if Account Id sets as array in Facebook Connection If set as True, each file will be exported in a separate file that has a prefix of account_id If set as False, a single file will be exported for all account_id :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). """
[docs] template_fields: Sequence[str] = ( "facebook_conn_id", "bucket_name", "object_name", "impersonation_chain", "parameters", )
def __init__( self, *, bucket_name: str, object_name: str, fields: list[str], parameters: dict[str, Any] | None = None, gzip: bool = False, upload_as_account: bool = False, api_version: str | None = None, gcp_conn_id: str = "google_cloud_default", facebook_conn_id: str = "facebook_default", impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.bucket_name = bucket_name self.object_name = object_name self.gcp_conn_id = gcp_conn_id self.facebook_conn_id = facebook_conn_id self.api_version = api_version self.fields = fields self.parameters = parameters self.gzip = gzip self.upload_as_account = upload_as_account self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context): service = FacebookAdsReportingHook( facebook_conn_id=self.facebook_conn_id, api_version=self.api_version ) bulk_report = service.bulk_facebook_report(params=self.parameters, fields=self.fields) if isinstance(bulk_report, list): converted_rows_with_action = self._generate_rows_with_action(False) converted_rows_with_action = self._prepare_rows_for_upload( rows=bulk_report, converted_rows_with_action=converted_rows_with_action, account_id=None ) elif isinstance(bulk_report, dict): converted_rows_with_action = self._generate_rows_with_action(True) for account_id in bulk_report.keys(): rows = bulk_report.get(account_id, []) if rows: converted_rows_with_action = self._prepare_rows_for_upload( rows=rows, converted_rows_with_action=converted_rows_with_action, account_id=account_id, ) else: self.log.warning("account_id: %s returned empty report", account_id) else: message = ( "Facebook Ads Hook returned different type than expected. Expected return types should be " f"List or Dict. Actual return type of the Hook: {type(bulk_report)}" ) raise AirflowException(message) total_row_count = self._decide_and_flush(converted_rows_with_action=converted_rows_with_action)"Facebook Returned %s data points in total: ", total_row_count)
def _generate_rows_with_action(self, type_check: bool): if type_check and self.upload_as_account: return {FlushAction.EXPORT_EVERY_ACCOUNT: []} else: return {FlushAction.EXPORT_ONCE: []} def _prepare_rows_for_upload( self, rows: list[AdsInsights], converted_rows_with_action: dict[FlushAction, list], account_id: str | None, ): converted_rows = [dict(row) for row in rows] if account_id is not None and self.upload_as_account: converted_rows_with_action[FlushAction.EXPORT_EVERY_ACCOUNT].append( {"account_id": account_id, "converted_rows": converted_rows} ) "Facebook Returned %s data points for account_id: %s", len(converted_rows), account_id ) else: converted_rows_with_action[FlushAction.EXPORT_ONCE].extend(converted_rows)"Facebook Returned %s data points ", len(converted_rows)) return converted_rows_with_action def _decide_and_flush(self, converted_rows_with_action: dict[FlushAction, list]): total_data_count = 0 once_action = converted_rows_with_action.get(FlushAction.EXPORT_ONCE) if once_action is not None: self._flush_rows( converted_rows=once_action, object_name=self.object_name, ) total_data_count += len(once_action) else: every_account_action = converted_rows_with_action.get(FlushAction.EXPORT_EVERY_ACCOUNT) if every_account_action: for converted_rows in every_account_action: self._flush_rows( converted_rows=converted_rows.get("converted_rows"), object_name=self._transform_object_name_with_account_id( account_id=converted_rows.get("account_id") ), ) total_data_count += len(converted_rows.get("converted_rows")) else: message = ( "FlushAction not found in the data. Please check the FlushAction in " f"the operator. Converted Rows with Action: {converted_rows_with_action}" ) raise AirflowException(message) return total_data_count def _flush_rows(self, converted_rows: list[Any] | None, object_name: str): if converted_rows: headers = converted_rows[0].keys() with tempfile.NamedTemporaryFile("w", suffix=".csv") as csvfile: writer = csv.DictWriter(csvfile, fieldnames=headers) writer.writeheader() 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_name, object_name=object_name,, gzip=self.gzip, )"%s uploaded to GCS", def _transform_object_name_with_account_id(self, account_id: str): directory_parts = self.object_name.split("/") directory_parts[-1] = f"{account_id}_{directory_parts[-1]}" return "/".join(directory_parts)

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