OpenMetadata/ingestion/examples/airflow/dags/airflow_sample_usage.py

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# Copyright 2025 Collate
# Licensed under the Collate Community License, Version 1.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# https://github.com/open-metadata/OpenMetadata/blob/main/ingestion/LICENSE
# 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.
import json
from datetime import timedelta
from airflow import DAG
try:
from airflow.operators.python import PythonOperator
except ModuleNotFoundError:
from airflow.operators.python_operator import PythonOperator
from airflow.utils.dates import days_ago
from metadata.workflow.usage import UsageWorkflow
default_args = {
"owner": "user_name",
"email": ["username@org.com"],
"email_on_failure": False,
"retries": 3,
"retry_delay": timedelta(seconds=10),
"execution_timeout": timedelta(minutes=60),
}
config = """
{
"source": {
"type": "custom-database",
"serviceName": "sample_data",
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"serviceConnection": {
"config": {
"type": "CustomDatabase",
"sourcePythonClass": "metadata.ingestion.source.database.sample_usage.SampleUsageSource",
"connectionOptions": {
"sampleDataFolder": "/home/airflow/ingestion/examples/sample_data"
}
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}
},
"sourceConfig": {
"config":{
"type": "DatabaseUsage"
}
}
},
"processor": {
"type": "query-parser",
"config": {}
},
"stage": {
"type": "table-usage",
"config": {
"filename": "/tmp/sample_usage"
}
},
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"bulkSink": {
"type": "metadata-usage",
"config": {
"filename": "/tmp/sample_usage"
}
},
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"workflowConfig": {
"openMetadataServerConfig": {
"hostPort": "http://openmetadata-server:8585/api",
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"authProvider": "openmetadata",
"securityConfig":{
"jwtToken": "eyJraWQiOiJHYjM4OWEtOWY3Ni1nZGpzLWE5MmotMDI0MmJrOTQzNTYiLCJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJzdWIiOiJhZG1pbiIsImlzQm90IjpmYWxzZSwiaXNzIjoib3Blbi1tZXRhZGF0YS5vcmciLCJpYXQiOjE2NjM5Mzg0NjIsImVtYWlsIjoiYWRtaW5Ab3Blbm1ldGFkYXRhLm9yZyJ9.tS8um_5DKu7HgzGBzS1VTA5uUjKWOCU0B_j08WXBiEC0mr0zNREkqVfwFDD-d24HlNEbrqioLsBuFRiwIWKc1m_ZlVQbG7P36RUxhuv2vbSp80FKyNM-Tj93FDzq91jsyNmsQhyNv_fNr3TXfzzSPjHt8Go0FMMP66weoKMgW2PbXlhVKwEuXUHyakLLzewm9UMeQaEiRzhiTMU3UkLXcKbYEJJvfNFcLwSl9W8JCO_l0Yj3ud-qt_nQYEZwqW6u5nfdQllN133iikV4fM5QZsMCnm8Rq1mvLR0y9bmJiD7fwM1tmJ791TUWqmKaTnP49U493VanKpUAfzIiOiIbhg"
}
}
}
}
"""
def metadata_ingestion_workflow():
workflow_config = json.loads(config)
workflow = UsageWorkflow.create(workflow_config)
workflow.execute()
workflow.raise_from_status()
workflow.print_status()
workflow.stop()
with DAG(
"sample_usage",
default_args=default_args,
description="An example DAG which runs a OpenMetadata ingestion workflow",
schedule_interval=timedelta(days=1),
start_date=days_ago(1),
is_paused_upon_creation=True,
catchup=False,
) as dag:
ingest_task = PythonOperator(
task_id="ingest_using_recipe",
python_callable=metadata_ingestion_workflow,
)