147 lines
5.5 KiB
Python
147 lines
5.5 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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DATA_CAT_TAGS = json.loads(os.environ.get("DATA_CAT_TAGS"))
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DWH_LAND_PRJ = os.environ.get("DWH_LAND_PRJ")
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DWH_LAND_BQ_DATASET = os.environ.get("DWH_LAND_BQ_DATASET")
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DWH_LAND_GCS = os.environ.get("DWH_LAND_GCS")
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DWH_CURATED_PRJ = os.environ.get("DWH_CURATED_PRJ")
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DWH_CURATED_BQ_DATASET = os.environ.get("DWH_CURATED_BQ_DATASET")
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DWH_CURATED_GCS = os.environ.get("DWH_CURATED_GCS")
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DWH_CONFIDENTIAL_PRJ = os.environ.get("DWH_CONFIDENTIAL_PRJ")
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DWH_CONFIDENTIAL_BQ_DATASET = os.environ.get("DWH_CONFIDENTIAL_BQ_DATASET")
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DWH_CONFIDENTIAL_GCS = os.environ.get("DWH_CONFIDENTIAL_GCS")
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DWH_PLG_PRJ = os.environ.get("DWH_PLG_PRJ")
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DWH_PLG_BQ_DATASET = os.environ.get("DWH_PLG_BQ_DATASET")
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DWH_PLG_GCS = os.environ.get("DWH_PLG_GCS")
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GCP_REGION = os.environ.get("GCP_REGION")
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DRP_PRJ = os.environ.get("DRP_PRJ")
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DRP_BQ = os.environ.get("DRP_BQ")
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DRP_GCS = os.environ.get("DRP_GCS")
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DRP_PS = os.environ.get("DRP_PS")
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LOD_PRJ = os.environ.get("LOD_PRJ")
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LOD_GCS_STAGING = os.environ.get("LOD_GCS_STAGING")
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LOD_NET_VPC = os.environ.get("LOD_NET_VPC")
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LOD_NET_SUBNET = os.environ.get("LOD_NET_SUBNET")
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LOD_SA_DF = os.environ.get("LOD_SA_DF")
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ORC_PRJ = os.environ.get("ORC_PRJ")
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ORC_GCS = os.environ.get("ORC_GCS")
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TRF_PRJ = os.environ.get("TRF_PRJ")
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TRF_GCS_STAGING = os.environ.get("TRF_GCS_STAGING")
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TRF_NET_VPC = os.environ.get("TRF_NET_VPC")
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TRF_NET_SUBNET = os.environ.get("TRF_NET_SUBNET")
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TRF_SA_DF = os.environ.get("TRF_SA_DF")
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TRF_SA_BQ = os.environ.get("TRF_SA_BQ")
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DF_KMS_KEY = os.environ.get("DF_KMS_KEY", "")
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DF_REGION = os.environ.get("GCP_REGION")
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DF_ZONE = os.environ.get("GCP_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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'dataflow_default_options': {
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'location': DF_REGION,
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'zone': DF_ZONE,
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'stagingLocation': LOD_GCS_STAGING,
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'tempLocation': LOD_GCS_STAGING + "/tmp",
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'serviceAccountEmail': LOD_SA_DF,
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'subnetwork': LOD_NET_SUBNET,
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'ipConfiguration': "WORKER_IP_PRIVATE",
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'kmsKeyName' : DF_KMS_KEY
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},
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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=DWH_LAND_PRJ+"."+DWH_LAND_BQ_DATASET+".customers",
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impersonation_chain=[LOD_SA_DF]
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)
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delete_table_purchases = BigQueryDeleteTableOperator(
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task_id="delete_table_purchases",
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deletion_dataset_table=DWH_LAND_PRJ+"."+DWH_LAND_BQ_DATASET+".purchases",
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impersonation_chain=[LOD_SA_DF]
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)
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delete_table_customer_purchase_curated = BigQueryDeleteTableOperator(
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task_id="delete_table_customer_purchase_curated",
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deletion_dataset_table=DWH_CURATED_PRJ+"."+DWH_CURATED_BQ_DATASET+".customer_purchase",
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impersonation_chain=[TRF_SA_DF]
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)
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delete_table_customer_purchase_confidential = BigQueryDeleteTableOperator(
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task_id="delete_table_customer_purchase_confidential",
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deletion_dataset_table=DWH_CONFIDENTIAL_PRJ+"."+DWH_CONFIDENTIAL_BQ_DATASET+".customer_purchase",
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impersonation_chain=[TRF_SA_DF]
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)
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start >> delte_table >> end
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