cloud-foundation-fabric/blueprints/data-solutions/data-platform-minimal/demo/dag_bq_gcs2bq.py

104 lines
3.7 KiB
Python

# Copyright 2022 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 csv
import datetime
import io
import json
import logging
import os
from airflow import models
from airflow.operators import dummy
from airflow.providers.google.cloud.transfers.gcs_to_bigquery import GCSToBigQueryOperator
# --------------------------------------------------------------------------------
# Set variables - Needed for the DEMO
# --------------------------------------------------------------------------------
BQ_LOCATION = os.environ.get("BQ_LOCATION")
CURATED_PRJ = os.environ.get("CURATED_PRJ")
CURATED_BQ_DATASET = os.environ.get("CURATED_BQ_DATASET")
CURATED_GCS = os.environ.get("CURATED_GCS")
LAND_PRJ = os.environ.get("LAND_PRJ")
LAND_GCS = os.environ.get("LAND_GCS")
PROCESSING_GCS = os.environ.get("PROCESSING_GCS")
PROCESSING_SA = os.environ.get("PROCESSING_SA")
PROCESSING_PRJ = os.environ.get("PROCESSING_PRJ")
PROCESSING_SUBNET = os.environ.get("PROCESSING_SUBNET")
PROCESSING_VPC = os.environ.get("PROCESSING_VPC")
DP_KMS_KEY = os.environ.get("DP_KMS_KEY", "")
DP_REGION = os.environ.get("DP_REGION")
DP_ZONE = os.environ.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 = dummy.DummyOperator(
task_id='start',
trigger_rule='all_success'
)
end = dummy.DummyOperator(
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, # The process will continue to run on the dataset project until the Apache Airflow bug is fixed. https://github.com/apache/airflow/issues/32106
impersonation_chain=[PROCESSING_SA]
)
start >> customers_import >> end