98 lines
3.4 KiB
Python
98 lines
3.4 KiB
Python
# Copyright 2022 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# --------------------------------------------------------------------------------
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# Load The Dependencies
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# --------------------------------------------------------------------------------
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import csv
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import datetime
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import io
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import json
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import logging
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import os
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from airflow import models
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from airflow.providers.google.cloud.operators.dataflow import DataflowTemplatedJobStartOperator
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from airflow.operators import dummy
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from airflow.providers.google.cloud.operators.bigquery import BigQueryDeleteTableOperator
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from airflow.utils.task_group import TaskGroup
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# --------------------------------------------------------------------------------
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# Set variables - Needed for the DEMO
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# --------------------------------------------------------------------------------
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BQ_LOCATION = os.environ.get("BQ_LOCATION")
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CURATED_PRJ = os.environ.get("CURATED_PRJ")
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CURATED_BQ_DATASET = os.environ.get("CURATED_BQ_DATASET")
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CURATED_GCS = os.environ.get("CURATED_GCS")
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LAND_PRJ = os.environ.get("LAND_PRJ")
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LAND_GCS = os.environ.get("LAND_GCS")
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PROCESSING_GCS = os.environ.get("PROCESSING_GCS")
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PROCESSING_SA = os.environ.get("PROCESSING_SA")
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PROCESSING_PRJ = os.environ.get("PROCESSING_PRJ")
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PROCESSING_SUBNET = os.environ.get("PROCESSING_SUBNET")
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PROCESSING_VPC = os.environ.get("PROCESSING_VPC")
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DP_KMS_KEY = os.environ.get("DP_KMS_KEY", "")
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DP_REGION = os.environ.get("DP_REGION")
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DP_ZONE = os.environ.get("DP_REGION") + "-b"
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# --------------------------------------------------------------------------------
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# Set default arguments
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# --------------------------------------------------------------------------------
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# If you are running Airflow in more than one time zone
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# see https://airflow.apache.org/docs/apache-airflow/stable/timezone.html
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# for best practices
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yesterday = datetime.datetime.now() - datetime.timedelta(days=1)
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default_args = {
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'owner': 'airflow',
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'start_date': yesterday,
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'depends_on_past': False,
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'email': [''],
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'email_on_failure': False,
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'email_on_retry': False,
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'retries': 1,
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'retry_delay': datetime.timedelta(minutes=5),
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}
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# --------------------------------------------------------------------------------
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# Main DAG
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# --------------------------------------------------------------------------------
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with models.DAG(
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'delete_tables_dag',
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default_args=default_args,
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schedule_interval=None) as dag:
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start = dummy.DummyOperator(
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task_id='start',
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trigger_rule='all_success'
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)
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end = dummy.DummyOperator(
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task_id='end',
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trigger_rule='all_success'
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)
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# Bigquery Tables deleted here for demo porpuse.
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# Consider a dedicated pipeline or tool for a real life scenario.
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with TaskGroup('delete_table') as delte_table:
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delete_table_customers = BigQueryDeleteTableOperator(
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task_id="delete_table_customers",
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deletion_dataset_table=CURATED_PRJ+"."+CURATED_BQ_DATASET+".customers",
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impersonation_chain=[PROCESSING_SA]
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)
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start >> delte_table >> end
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