Run this DAG

1. Install the Astronomer CLI:Skip if you already have the CLI

2. Initate the project in a local directory:

3. Copy and paste the code below into a file in the

dags
directory.

4. Add the following to your

requirements.txt
file:

5. Run the DAG from the local directory where the project was initiated:

#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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
#
# http://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.
"""
Example Airflow DAG for Google BigQuery Sensors.
"""
import os
from datetime import datetime
from airflow import models
from airflow.providers.google.cloud.operators.bigquery import (
BigQueryCreateEmptyDatasetOperator,
BigQueryCreateEmptyTableOperator,
BigQueryDeleteDatasetOperator,
BigQueryExecuteQueryOperator,
)
from airflow.providers.google.cloud.sensors.bigquery import (
BigQueryTableExistenceSensor,
BigQueryTablePartitionExistenceSensor,
)
from airflow.utils.dates import days_ago
PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "example-project")
DATASET_NAME = os.environ.get("GCP_BIGQUERY_DATASET_NAME", "test_sensors_dataset")
TABLE_NAME = "partitioned_table"
INSERT_DATE = datetime.now().strftime("%Y-%m-%d")
PARTITION_NAME = "{{ ds_nodash }}"
INSERT_ROWS_QUERY = f"INSERT {DATASET_NAME}.{TABLE_NAME} VALUES " "(42, '{{ ds }}')"
SCHEMA = [
{"name": "value", "type": "INTEGER", "mode": "REQUIRED"},
{"name": "ds", "type": "DATE", "mode": "NULLABLE"},
]
dag_id = "example_bigquery_sensors"
with models.DAG(
dag_id,
schedule_interval=None, # Override to match your needs
start_date=days_ago(1),
tags=["example"],
user_defined_macros={"DATASET": DATASET_NAME, "TABLE": TABLE_NAME},
default_args={"project_id": PROJECT_ID},
) as dag_with_locations:
create_dataset = BigQueryCreateEmptyDatasetOperator(
task_id="create-dataset", dataset_id=DATASET_NAME, project_id=PROJECT_ID
)
create_table = BigQueryCreateEmptyTableOperator(
task_id="create_table",
dataset_id=DATASET_NAME,
table_id=TABLE_NAME,
schema_fields=SCHEMA,
time_partitioning={
"type": "DAY",
"field": "ds",
},
)
# [START howto_sensor_bigquery_table]
check_table_exists = BigQueryTableExistenceSensor(
task_id="check_table_exists", project_id=PROJECT_ID, dataset_id=DATASET_NAME, table_id=TABLE_NAME
)
# [END howto_sensor_bigquery_table]
execute_insert_query = BigQueryExecuteQueryOperator(
task_id="execute_insert_query", sql=INSERT_ROWS_QUERY, use_legacy_sql=False
)
# [START howto_sensor_bigquery_table_partition]
check_table_partition_exists = BigQueryTablePartitionExistenceSensor(
task_id="check_table_partition_exists",
project_id=PROJECT_ID,
dataset_id=DATASET_NAME,
table_id=TABLE_NAME,
partition_id=PARTITION_NAME,
)
# [END howto_sensor_bigquery_table_partition]
delete_dataset = BigQueryDeleteDatasetOperator(
task_id="delete_dataset", dataset_id=DATASET_NAME, delete_contents=True
)
create_dataset >> create_table
create_table >> check_table_exists
create_table >> execute_insert_query
execute_insert_query >> check_table_partition_exists
check_table_exists >> delete_dataset
check_table_partition_exists >> delete_dataset