mirror of
https://github.com/open-metadata/OpenMetadata.git
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90 lines
3.1 KiB
Python
90 lines
3.1 KiB
Python
# Copyright 2025 Collate
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# Licensed under the Collate Community License, Version 1.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# https://github.com/open-metadata/OpenMetadata/blob/main/ingestion/LICENSE
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This DAG can be used directly in your Airflow instance after installing
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the `openmetadata-ingestion[airflow-container]` package. Its purpose
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is to connect to the underlying database, retrieve the information
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and push it to OpenMetadata.
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"""
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from datetime import timedelta
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import yaml
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from airflow import DAG
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try:
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from airflow.operators.python import PythonOperator
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except ModuleNotFoundError:
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from airflow.operators.python_operator import PythonOperator
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from airflow.utils.dates import days_ago
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from metadata.workflow.metadata import MetadataWorkflow
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default_args = {
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"owner": "user_name",
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"email": ["username@org.com"],
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"email_on_failure": False,
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"retries": 3,
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"retry_delay": timedelta(minutes=5),
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"execution_timeout": timedelta(minutes=60),
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}
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config = """
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source:
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type: airflow
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serviceName: airflow_source
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serviceConnection:
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config:
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type: Airflow
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hostPort: http://localhost:8080
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numberOfStatus: 10
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connection:
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type: Backend
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sourceConfig:
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config:
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type: PipelineMetadata
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sink:
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type: metadata-rest
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config: {}
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workflowConfig:
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loggerLevel: INFO
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openMetadataServerConfig:
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hostPort: http://openmetadata-server:8585/api
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authProvider: openmetadata
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securityConfig:
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jwtToken: "eyJraWQiOiJHYjM4OWEtOWY3Ni1nZGpzLWE5MmotMDI0MmJrOTQzNTYiLCJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJzdWIiOiJhZG1pbiIsImlzQm90IjpmYWxzZSwiaXNzIjoib3Blbi1tZXRhZGF0YS5vcmciLCJpYXQiOjE2NjM5Mzg0NjIsImVtYWlsIjoiYWRtaW5Ab3Blbm1ldGFkYXRhLm9yZyJ9.tS8um_5DKu7HgzGBzS1VTA5uUjKWOCU0B_j08WXBiEC0mr0zNREkqVfwFDD-d24HlNEbrqioLsBuFRiwIWKc1m_ZlVQbG7P36RUxhuv2vbSp80FKyNM-Tj93FDzq91jsyNmsQhyNv_fNr3TXfzzSPjHt8Go0FMMP66weoKMgW2PbXlhVKwEuXUHyakLLzewm9UMeQaEiRzhiTMU3UkLXcKbYEJJvfNFcLwSl9W8JCO_l0Yj3ud-qt_nQYEZwqW6u5nfdQllN133iikV4fM5QZsMCnm8Rq1mvLR0y9bmJiD7fwM1tmJ791TUWqmKaTnP49U493VanKpUAfzIiOiIbhg"
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"""
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def metadata_ingestion_workflow():
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workflow_config = yaml.safe_load(config)
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workflow = MetadataWorkflow.create(workflow_config)
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workflow.execute()
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workflow.raise_from_status()
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workflow.print_status()
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workflow.stop()
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with DAG(
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"airflow_metadata_extraction",
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default_args=default_args,
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description="An example DAG which pushes Airflow data to OM",
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start_date=days_ago(1),
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is_paused_upon_creation=True,
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schedule_interval="*/5 * * * *",
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catchup=False,
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) as dag:
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ingest_task = PythonOperator(
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task_id="ingest_using_recipe",
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python_callable=metadata_ingestion_workflow,
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)
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