Run this DAG

1. Install Astronomer CLISkip if you already have the CLI

2. Initate the project:

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:

# 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 that shows how to use DataFusion.
"""
import os
from airflow import models
from airflow.operators.bash import BashOperator
from airflow.providers.google.cloud.operators.datafusion import (
CloudDataFusionCreateInstanceOperator,
CloudDataFusionCreatePipelineOperator,
CloudDataFusionDeleteInstanceOperator,
CloudDataFusionDeletePipelineOperator,
CloudDataFusionGetInstanceOperator,
CloudDataFusionListPipelinesOperator,
CloudDataFusionRestartInstanceOperator,
CloudDataFusionStartPipelineOperator,
CloudDataFusionStopPipelineOperator,
CloudDataFusionUpdateInstanceOperator,
)
from airflow.utils import dates
from airflow.utils.state import State
# [START howto_data_fusion_env_variables]
LOCATION = "europe-north1"
INSTANCE_NAME = "airflow-test-instance"
INSTANCE = {"type": "BASIC", "displayName": INSTANCE_NAME}
BUCKET_1 = os.environ.get("GCP_DATAFUSION_BUCKET_1", "test-datafusion-bucket-1")
BUCKET_2 = os.environ.get("GCP_DATAFUSION_BUCKET_2", "test-datafusion-bucket-2")
BUCKET_1_URI = f"gs//{BUCKET_1}"
BUCKET_2_URI = f"gs//{BUCKET_2}"
PIPELINE_NAME = os.environ.get("GCP_DATAFUSION_PIPELINE_NAME", "airflow_test")
PIPELINE = {
"name": "test-pipe",
"description": "Data Pipeline Application",
"artifact": {"name": "cdap-data-pipeline", "version": "6.1.2", "scope": "SYSTEM"},
"config": {
"resources": {"memoryMB": 2048, "virtualCores": 1},
"driverResources": {"memoryMB": 2048, "virtualCores": 1},
"connections": [{"from": "GCS", "to": "GCS2"}],
"comments": [],
"postActions": [],
"properties": {},
"processTimingEnabled": True,
"stageLoggingEnabled": False,
"stages": [
{
"name": "GCS",
"plugin": {
"name": "GCSFile",
"type": "batchsource",
"label": "GCS",
"artifact": {
"name": "google-cloud",
"version": "0.14.2",
"scope": "SYSTEM",
},
"properties": {
"project": "auto-detect",
"format": "text",
"skipHeader": "false",
"serviceFilePath": "auto-detect",
"filenameOnly": "false",
"recursive": "false",
"encrypted": "false",
"schema": '{"type":"record","name":"etlSchemaBody","fields":'
'[{"name":"offset","type":"long"},{"name":"body","type":"string"}]}',
"path": BUCKET_1_URI,
"referenceName": "foo_bucket",
},
},
"outputSchema": [
{
"name": "etlSchemaBody",
"schema": '{"type":"record","name":"etlSchemaBody","fields":'
'[{"name":"offset","type":"long"},{"name":"body","type":"string"}]}',
}
],
},
{
"name": "GCS2",
"plugin": {
"name": "GCS",
"type": "batchsink",
"label": "GCS2",
"artifact": {
"name": "google-cloud",
"version": "0.14.2",
"scope": "SYSTEM",
},
"properties": {
"project": "auto-detect",
"suffix": "yyyy-MM-dd-HH-mm",
"format": "json",
"serviceFilePath": "auto-detect",
"location": "us",
"schema": '{"type":"record","name":"etlSchemaBody","fields":'
'[{"name":"offset","type":"long"},{"name":"body","type":"string"}]}',
"referenceName": "bar",
"path": BUCKET_2_URI,
},
},
"outputSchema": [
{
"name": "etlSchemaBody",
"schema": '{"type":"record","name":"etlSchemaBody","fields":'
'[{"name":"offset","type":"long"},{"name":"body","type":"string"}]}',
}
],
"inputSchema": [
{
"name": "GCS",
"schema": '{"type":"record","name":"etlSchemaBody","fields":'
'[{"name":"offset","type":"long"},{"name":"body","type":"string"}]}',
}
],
},
],
"schedule": "0 * * * *",
"engine": "spark",
"numOfRecordsPreview": 100,
"maxConcurrentRuns": 1,
},
}
# [END howto_data_fusion_env_variables]
with models.DAG(
"example_data_fusion",
schedule_interval=None, # Override to match your needs
start_date=dates.days_ago(1),
) as dag:
# [START howto_cloud_data_fusion_create_instance_operator]
create_instance = CloudDataFusionCreateInstanceOperator(
location=LOCATION,
instance_name=INSTANCE_NAME,
instance=INSTANCE,
task_id="create_instance",
)
# [END howto_cloud_data_fusion_create_instance_operator]
# [START howto_cloud_data_fusion_get_instance_operator]
get_instance = CloudDataFusionGetInstanceOperator(
location=LOCATION, instance_name=INSTANCE_NAME, task_id="get_instance"
)
# [END howto_cloud_data_fusion_get_instance_operator]
# [START howto_cloud_data_fusion_restart_instance_operator]
restart_instance = CloudDataFusionRestartInstanceOperator(
location=LOCATION, instance_name=INSTANCE_NAME, task_id="restart_instance"
)
# [END howto_cloud_data_fusion_restart_instance_operator]
# [START howto_cloud_data_fusion_update_instance_operator]
update_instance = CloudDataFusionUpdateInstanceOperator(
location=LOCATION,
instance_name=INSTANCE_NAME,
instance=INSTANCE,
update_mask="instance.displayName",
task_id="update_instance",
)
# [END howto_cloud_data_fusion_update_instance_operator]
# [START howto_cloud_data_fusion_create_pipeline]
create_pipeline = CloudDataFusionCreatePipelineOperator(
location=LOCATION,
pipeline_name=PIPELINE_NAME,
pipeline=PIPELINE,
instance_name=INSTANCE_NAME,
task_id="create_pipeline",
)
# [END howto_cloud_data_fusion_create_pipeline]
# [START howto_cloud_data_fusion_list_pipelines]
list_pipelines = CloudDataFusionListPipelinesOperator(
location=LOCATION, instance_name=INSTANCE_NAME, task_id="list_pipelines"
)
# [END howto_cloud_data_fusion_list_pipelines]
# [START howto_cloud_data_fusion_start_pipeline]
start_pipeline = CloudDataFusionStartPipelineOperator(
location=LOCATION,
pipeline_name=PIPELINE_NAME,
instance_name=INSTANCE_NAME,
task_id="start_pipeline",
)
# [END howto_cloud_data_fusion_start_pipeline]
# [START howto_cloud_data_fusion_stop_pipeline]
stop_pipeline = CloudDataFusionStopPipelineOperator(
location=LOCATION,
pipeline_name=PIPELINE_NAME,
instance_name=INSTANCE_NAME,
task_id="stop_pipeline",
)
# [END howto_cloud_data_fusion_stop_pipeline]
# [START howto_cloud_data_fusion_delete_pipeline]
delete_pipeline = CloudDataFusionDeletePipelineOperator(
location=LOCATION,
pipeline_name=PIPELINE_NAME,
instance_name=INSTANCE_NAME,
task_id="delete_pipeline",
)
# [END howto_cloud_data_fusion_delete_pipeline]
# [START howto_cloud_data_fusion_delete_instance_operator]
delete_instance = CloudDataFusionDeleteInstanceOperator(
location=LOCATION, instance_name=INSTANCE_NAME, task_id="delete_instance"
)
# [END howto_cloud_data_fusion_delete_instance_operator]
# Add sleep before creating pipeline
sleep = BashOperator(task_id="sleep", bash_command="sleep 60")
create_instance >> get_instance >> restart_instance >> update_instance >> sleep
sleep >> create_pipeline >> list_pipelines >> start_pipeline >> stop_pipeline >> delete_pipeline
delete_pipeline >> delete_instance
if __name__ == "__main__":
dag.clear(dag_run_state=State.NONE)
dag.run()