104 lines
3.7 KiB
Python
104 lines
3.7 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.operators import dummy
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from airflow.providers.google.cloud.transfers.gcs_to_bigquery import GCSToBigQueryOperator
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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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'bq_gcs2bq',
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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 automatically created for demo porpuse.
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# Consider a dedicated pipeline or tool for a real life scenario.
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customers_import = GCSToBigQueryOperator(
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task_id='csv_to_bigquery',
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bucket=LAND_GCS[5:],
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source_objects=['customers.csv'],
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destination_project_dataset_table='{}:{}.{}'.format(CURATED_PRJ, CURATED_BQ_DATASET, 'customers'),
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create_disposition='CREATE_IF_NEEDED',
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write_disposition='WRITE_APPEND',
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schema_update_options=['ALLOW_FIELD_RELAXATION', 'ALLOW_FIELD_ADDITION'],
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schema_object="customers.json",
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schema_object_bucket=PROCESSING_GCS[5:],
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project_id=PROCESSING_PRJ, # The process will continue to run on the dataset project until the Apache Airflow bug is fixed. https://github.com/apache/airflow/issues/32106
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impersonation_chain=[PROCESSING_SA]
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)
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start >> customers_import >> end
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