airflow example_datafusion 源码

airflow example_datafusion 代码

文件路径：/airflow/providers/google/cloud/example_dags/example_datafusion.py

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# to you under the Apache License, Version 2.0 (the
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# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
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# specific language governing permissions and limitations
# under the License.
"""
Example Airflow DAG that shows how to use DataFusion.
"""
from __future__ import annotations

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]
SERVICE_ACCOUNT = os.environ.get("GCP_DATAFUSION_SERVICE_ACCOUNT")
LOCATION = "europe-north1"
INSTANCE_NAME = "airflow-test-instance"
INSTANCE = {
    "type": "BASIC",
    "displayName": INSTANCE_NAME,
    "dataprocServiceAccount": SERVICE_ACCOUNT,
}

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 = {
    "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",
    start_date=datetime(2021, 1, 1),
    catchup=False,
) 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="",
        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()

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