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"""
Example Airflow DAG that shows how to use DataFusion.
"""
import os
from datetime import datetime
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.providers.google.cloud.sensors.datafusion import CloudDataFusionPipelineStateSensor
# [START howto_data_fusion_env_variables]
[docs]SERVICE_ACCOUNT = os.environ.get("GCP_DATAFUSION_SERVICE_ACCOUNT")
[docs]LOCATION = "europe-north1"
[docs]INSTANCE_NAME = "airflow-test-instance"
[docs]INSTANCE = {
"type": "BASIC",
"displayName": INSTANCE_NAME,
"dataprocServiceAccount": SERVICE_ACCOUNT,
}
[docs]BUCKET_1 = os.environ.get("GCP_DATAFUSION_BUCKET_1", "test-datafusion-bucket-1")
[docs]BUCKET_2 = os.environ.get("GCP_DATAFUSION_BUCKET_2", "test-datafusion-bucket-2")
[docs]BUCKET_1_URI = f"gs://{BUCKET_1}"
[docs]BUCKET_2_URI = f"gs://{BUCKET_2}"
[docs]PIPELINE_NAME = os.environ.get("GCP_DATAFUSION_PIPELINE_NAME", "airflow_test")
[docs]PIPELINE = {
"artifact": {
"name": "cdap-data-pipeline",
"version": "6.5.1",
"scope": "SYSTEM",
"label": "Data Pipeline - System Test",
},
"description": "Data Pipeline Application",
"name": "test-pipe",
"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": "0.18.1", "scope": "SYSTEM"},
"properties": {
"project": "auto-detect",
"format": "text",
"skipHeader": "false",
"serviceFilePath": "auto-detect",
"filenameOnly": "false",
"recursive": "false",
"encrypted": "false",
"schema": "{\"type\":\"record\",\"name\":\"textfile\",\"fields\":[{\"name\"\
:\"offset\",\"type\":\"long\"},{\"name\":\"body\",\"type\":\"string\"}]}",
"path": BUCKET_1_URI,
"referenceName": "foo_bucket",
"useConnection": "false",
"serviceAccountType": "filePath",
"sampleSize": "1000",
"fileEncoding": "UTF-8",
},
},
"outputSchema": "{\"type\":\"record\",\"name\":\"textfile\",\"fields\"\
:[{\"name\":\"offset\",\"type\":\"long\"},{\"name\":\"body\",\"type\":\"string\"}]}",
"id": "GCS",
},
{
"name": "GCS2",
"plugin": {
"name": "GCS",
"type": "batchsink",
"label": "GCS2",
"artifact": {"name": "google-cloud", "version": "0.18.1", "scope": "SYSTEM"},
"properties": {
"project": "auto-detect",
"suffix": "yyyy-MM-dd-HH-mm",
"format": "json",
"serviceFilePath": "auto-detect",
"location": "us",
"schema": "{\"type\":\"record\",\"name\":\"textfile\",\"fields\":[{\"name\"\
:\"offset\",\"type\":\"long\"},{\"name\":\"body\",\"type\":\"string\"}]}",
"referenceName": "bar",
"path": BUCKET_2_URI,
"serviceAccountType": "filePath",
"contentType": "application/octet-stream",
},
},
"outputSchema": "{\"type\":\"record\",\"name\":\"textfile\",\"fields\"\
:[{\"name\":\"offset\",\"type\":\"long\"},{\"name\":\"body\",\"type\":\"string\"}]}",
"inputSchema": [
{
"name": "GCS",
"schema": "{\"type\":\"record\",\"name\":\"textfile\",\"fields\":[{\"name\"\
:\"offset\",\"type\":\"long\"},{\"name\":\"body\",\"type\":\"string\"}]}",
}
],
"id": "GCS2",
},
],
"schedule": "0 * * * *",
"engine": "spark",
"numOfRecordsPreview": 100,
"description": "Data Pipeline Application",
"maxConcurrentRuns": 1,
},
}
# [END howto_data_fusion_env_variables]
with models.DAG(
"example_data_fusion",
schedule_interval='@once', # Override to match your needs
start_date=datetime(2021, 1, 1),
catchup=False,
) as dag:
# [START howto_cloud_data_fusion_create_instance_operator]
[docs] 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="",
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_start_pipeline_async]
start_pipeline_async = CloudDataFusionStartPipelineOperator(
location=LOCATION,
pipeline_name=PIPELINE_NAME,
instance_name=INSTANCE_NAME,
asynchronous=True,
task_id="start_pipeline_async",
)
# [END howto_cloud_data_fusion_start_pipeline_async]
# [START howto_cloud_data_fusion_start_pipeline_sensor]
start_pipeline_sensor = CloudDataFusionPipelineStateSensor(
task_id="pipeline_state_sensor",
pipeline_name=PIPELINE_NAME,
pipeline_id=start_pipeline_async.output,
expected_statuses=["COMPLETED"],
failure_statuses=["FAILED"],
instance_name=INSTANCE_NAME,
location=LOCATION,
)
# [END howto_cloud_data_fusion_start_pipeline_sensor]
# [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_async
>> start_pipeline_sensor
>> start_pipeline
>> stop_pipeline
>> delete_pipeline
)
delete_pipeline >> delete_instance
if __name__ == "__main__":
dag.clear()
dag.run()
