mirror of
https://github.com/open-metadata/OpenMetadata.git
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97fad806a2
* feat(app): add config to status add config to the reported status of the ingestion pipeline * added separate pipeline service client call for external apps * fix masking of pydantic model * - overload model_dump to mask secrets instead of a separate method - moved tests to test_custom_pydantic.py * fix: execution time * fix: mask secrets in dump json * fix: for python3.8 * fix: for python3.8 * fix: use mask_secrets=False when dumping a model for create * format * fix: update mask_secrets=False for workflow configurations * fix: use context directly when using model_dump_json * fix: default behavior when dumping json * format * fixed tests
107 lines
3.5 KiB
Python
107 lines
3.5 KiB
Python
# Copyright 2021 Collate
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# Licensed under the Apache License, Version 2.0 (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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# http://www.apache.org/licenses/LICENSE-2.0
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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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Generic Workflow entrypoint to execute Applications
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"""
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import json
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from airflow import DAG
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from openmetadata_managed_apis.utils.logger import set_operator_logger
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from openmetadata_managed_apis.workflows.ingestion.common import (
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build_dag,
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build_workflow_config_property,
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)
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from metadata.generated.schema.entity.applications.configuration.applicationConfig import (
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AppConfig,
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PrivateConfig,
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)
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from metadata.generated.schema.entity.services.ingestionPipelines.ingestionPipeline import (
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IngestionPipeline,
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)
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from metadata.generated.schema.metadataIngestion.application import (
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OpenMetadataApplicationConfig,
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)
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from metadata.generated.schema.metadataIngestion.applicationPipeline import (
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ApplicationPipeline,
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)
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from metadata.workflow.application import ApplicationWorkflow
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def application_workflow(workflow_config: OpenMetadataApplicationConfig):
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"""
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Task that creates and runs the ingestion workflow.
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The workflow_config gets cooked form the incoming
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ingestionPipeline.
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This is the callable used to create the PythonOperator
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"""
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set_operator_logger(workflow_config)
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config = json.loads(
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workflow_config.model_dump_json(exclude_defaults=False, mask_secrets=False)
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)
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workflow = ApplicationWorkflow.create(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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def build_application_workflow_config(
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ingestion_pipeline: IngestionPipeline,
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) -> OpenMetadataApplicationConfig:
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"""
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Given an airflow_pipeline, prepare the workflow config JSON
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"""
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# Here we have an application pipeline, so the Source Config is of type ApplicationPipeline
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application_pipeline_conf: ApplicationPipeline = (
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ingestion_pipeline.sourceConfig.config
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)
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application_workflow_config = OpenMetadataApplicationConfig(
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sourcePythonClass=application_pipeline_conf.sourcePythonClass,
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# We pass the generic class and let each app cast the actual object
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appConfig=AppConfig(
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root=application_pipeline_conf.appConfig.root,
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)
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if application_pipeline_conf.appConfig
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else None,
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appPrivateConfig=PrivateConfig(
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root=application_pipeline_conf.appPrivateConfig.root
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)
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if application_pipeline_conf.appPrivateConfig
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else None,
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workflowConfig=build_workflow_config_property(ingestion_pipeline),
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ingestionPipelineFQN=ingestion_pipeline.fullyQualifiedName.root,
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)
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return application_workflow_config
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def build_application_dag(ingestion_pipeline: IngestionPipeline) -> DAG:
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"""
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Build a simple metadata workflow DAG
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"""
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application_workflow_config = build_application_workflow_config(ingestion_pipeline)
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dag = build_dag(
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task_name="application_task",
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ingestion_pipeline=ingestion_pipeline,
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workflow_config=application_workflow_config,
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workflow_fn=application_workflow,
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)
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return dag
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