mirror of
https://github.com/open-metadata/OpenMetadata.git
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6ca71ae323
* feat: updated DI workflow to inherit from BaseWorkflow + split processor and producer classes * feat: __init__.py files creation * feat: updated workflow import classes in code and doc * feat: moved kpi runner from runner to processor folder * fix: skip failure on list entities * feat: deleted unused files * feat: updated status reporter * feat: ran linting * feat: fix test error with typing and fqn * feat: updated test dependencies * feat: ran linting * feat: move execution order up * feat: updated cost analysis report to align with new workflow * feat: fix entity already exists for pipeline entity status * feat: ran python linting * feat: move skip_on_failure to method * feat: ran linting --------- Co-authored-by: Sriharsha Chintalapani <harshach@users.noreply.github.com>
126 lines
4.3 KiB
Python
126 lines
4.3 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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Data Insights DAG function builder
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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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ClientInitializationError,
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GetServiceException,
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build_dag,
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)
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from openmetadata_managed_apis.workflows.ingestion.elasticsearch_sink import (
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build_elasticsearch_sink,
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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.entity.services.metadataService import MetadataService
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from metadata.generated.schema.metadataIngestion.workflow import (
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LogLevels,
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OpenMetadataWorkflowConfig,
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Processor,
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)
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from metadata.generated.schema.metadataIngestion.workflow import (
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Source as WorkflowSource,
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)
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from metadata.generated.schema.metadataIngestion.workflow import (
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SourceConfig,
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WorkflowConfig,
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)
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from metadata.ingestion.models.encoders import show_secrets_encoder
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from metadata.ingestion.ometa.ometa_api import OpenMetadata
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from metadata.workflow.data_insight import DataInsightWorkflow
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from metadata.workflow.workflow_output_handler import print_data_insight_status
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def data_insight_workflow(workflow_config: OpenMetadataWorkflowConfig):
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"""Task that creates and runs the data insight workflow.
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The workflow_config gets created 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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Args:
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workflow_config (OpenMetadataWorkflowConfig): _description_
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"""
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set_operator_logger(workflow_config)
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config = json.loads(workflow_config.json(encoder=show_secrets_encoder))
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workflow = DataInsightWorkflow.create(config)
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workflow.execute()
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workflow.raise_from_status()
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print_data_insight_status(workflow)
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workflow.stop()
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def build_data_insight_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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try:
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metadata = OpenMetadata(config=ingestion_pipeline.openMetadataServerConnection)
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except Exception as exc:
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raise ClientInitializationError(f"Failed to initialize the client: {exc}")
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openmetadata_service = metadata.get_by_name(
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entity=MetadataService, fqn=ingestion_pipeline.service.fullyQualifiedName
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)
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if not openmetadata_service:
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raise GetServiceException(service_type="metadata", service_name="OpenMetadata")
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sink = build_elasticsearch_sink(
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openmetadata_service.connection.config, ingestion_pipeline
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)
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workflow_config = OpenMetadataWorkflowConfig(
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source=WorkflowSource(
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type="dataInsight",
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serviceName=ingestion_pipeline.service.name,
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sourceConfig=SourceConfig(), # Source Config not needed here. Configs are passed to ES Sink.
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),
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sink=sink,
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processor=Processor(
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type="data-insight-processor",
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config={},
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),
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workflowConfig=WorkflowConfig(
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loggerLevel=ingestion_pipeline.loggerLevel or LogLevels.INFO,
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openMetadataServerConfig=ingestion_pipeline.openMetadataServerConnection,
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),
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ingestionPipelineFQN=ingestion_pipeline.fullyQualifiedName.__root__,
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)
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return workflow_config
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def build_data_insight_dag(ingestion_pipeline: IngestionPipeline) -> DAG:
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"""Build a simple Data Insight DAG"""
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workflow_config = build_data_insight_workflow_config(ingestion_pipeline)
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dag = build_dag(
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task_name="data_insight_task",
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ingestion_pipeline=ingestion_pipeline,
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workflow_config=workflow_config,
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workflow_fn=data_insight_workflow,
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)
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return dag
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