dag_bq_gcs2bq.py

# Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# --------------------------------------------------------------------------------
# Load The Dependencies
# --------------------------------------------------------------------------------

import datetime

from airflow import models
from airflow.models.variable import Variable
from airflow.operators import empty
from airflow.providers.google.cloud.transfers.gcs_to_bigquery import \
  GCSToBigQueryOperator

# --------------------------------------------------------------------------------
# Set variables - Needed for the DEMO
# --------------------------------------------------------------------------------
BQ_LOCATION = Variable.get("BQ_LOCATION")
CURATED_PRJ = Variable.get("CURATED_PRJ")
CURATED_BQ_DATASET = Variable.get("CURATED_BQ_DATASET")
CURATED_GCS = Variable.get("CURATED_GCS")
LAND_PRJ = Variable.get("LAND_PRJ")
LAND_GCS = Variable.get("LAND_GCS")
PROCESSING_GCS = Variable.get("PROCESSING_GCS")
PROCESSING_SA = Variable.get("PROCESSING_SA")
PROCESSING_PRJ = Variable.get("PROCESSING_PRJ")
PROCESSING_SUBNET = Variable.get("PROCESSING_SUBNET")
PROCESSING_VPC = Variable.get("PROCESSING_VPC")
DP_KMS_KEY = Variable.get("DP_KMS_KEY", "")
DP_REGION = Variable.get("DP_REGION")
DP_ZONE = Variable.get("DP_REGION") + "-b"

# --------------------------------------------------------------------------------
# Set default arguments
# --------------------------------------------------------------------------------

# If you are running Airflow in more than one time zone
# see https://airflow.apache.org/docs/apache-airflow/stable/timezone.html
# for best practices
yesterday = datetime.datetime.now() - datetime.timedelta(days=1)

default_args = {
    'owner': 'airflow',
    'start_date': yesterday,
    'depends_on_past': False,
    'email': [''],
    'email_on_failure': False,
    'email_on_retry': False,
    'retries': 1,
    'retry_delay': datetime.timedelta(minutes=5),
}

# --------------------------------------------------------------------------------
# Main DAG
# --------------------------------------------------------------------------------

with models.DAG('bq_gcs2bq', default_args=default_args,
                schedule_interval=None) as dag:
  start = empty.EmptyOperator(task_id='start', trigger_rule='all_success')

  end = empty.EmptyOperator(task_id='end', trigger_rule='all_success')

  # Bigquery Tables automatically created for demo porpuse.
  # Consider a dedicated pipeline or tool for a real life scenario.

  customers_import = GCSToBigQueryOperator(
      task_id='csv_to_bigquery', bucket=LAND_GCS[5:],
      source_objects=['customers.csv'
                     ], destination_project_dataset_table='{}:{}.{}'.format(
                         CURATED_PRJ, CURATED_BQ_DATASET, 'customers'),
      create_disposition='CREATE_IF_NEEDED', write_disposition='WRITE_APPEND',
      schema_update_options=['ALLOW_FIELD_RELAXATION', 'ALLOW_FIELD_ADDITION'],
      schema_object="customers.json", schema_object_bucket=PROCESSING_GCS[5:],
      project_id=PROCESSING_PRJ, impersonation_chain=[PROCESSING_SA])

  start >> customers_import >> end