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https://github.com/open-metadata/OpenMetadata.git
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c522f14178
* Refactor output_handlers to a WorkflowOutputHandler class * Add old methods as deprecated to avoid breaking changes * Extract WorkflowInitErrorHandler from workflow_output_handler * Fix static checks * Fix tests * Fix tests * Update code based on comments from PR * Update comment
119 lines
3.6 KiB
Python
119 lines
3.6 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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Metadata DAG function builder
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"""
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import json
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import tempfile
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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_source,
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build_workflow_config_property,
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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.workflow import (
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BulkSink,
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OpenMetadataWorkflowConfig,
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Processor,
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Stage,
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)
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from metadata.workflow.usage import UsageWorkflow
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def usage_workflow(workflow_config: OpenMetadataWorkflowConfig):
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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(workflow_config.model_dump_json(exclude_defaults=False))
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workflow = UsageWorkflow.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_usage_config_from_file(
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ingestion_pipeline: IngestionPipeline, filename: str
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) -> OpenMetadataWorkflowConfig:
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"""
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Given a filename for the staging location, build
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the OpenMetadataWorkflowConfig
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:param ingestion_pipeline: IngestionPipeline with workflow info
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:param filename: staging location file
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:return: OpenMetadataWorkflowConfig
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"""
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source = build_source(ingestion_pipeline)
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source.type = f"{source.type}-usage" # Mark the source as usage
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return OpenMetadataWorkflowConfig(
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source=source,
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processor=Processor(type="query-parser", config={}),
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stage=Stage(
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type="table-usage",
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config={"filename": filename},
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),
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bulkSink=BulkSink(
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type="metadata-usage",
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config={"filename": filename},
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),
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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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def build_usage_workflow_config(
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ingestion_pipeline: IngestionPipeline,
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) -> OpenMetadataWorkflowConfig:
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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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location = ingestion_pipeline.sourceConfig.config.stageFileLocation
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if not location:
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with tempfile.NamedTemporaryFile() as tmp_file:
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workflow_config = build_usage_config_from_file(ingestion_pipeline, tmp_file)
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else:
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workflow_config = build_usage_config_from_file(ingestion_pipeline, location)
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return workflow_config
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def build_usage_dag(airflow_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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workflow_config = build_usage_workflow_config(airflow_pipeline)
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dag = build_dag(
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task_name="usage_task",
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ingestion_pipeline=airflow_pipeline,
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workflow_config=workflow_config,
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workflow_fn=usage_workflow,
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)
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return dag
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