mirror of
https://github.com/open-metadata/OpenMetadata.git
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f2fb0521c2
* Update airflow loggers and rename ometa loggers * ANSI print to logger * Remove colored logging from tests * Merge ometa_logger into the one used in loggers class * linting * linting Co-authored-by: Nahuel Verdugo Revigliono <nahuel@getcollate.io>
266 lines
8.9 KiB
Python
266 lines
8.9 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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Databricks Pipeline utils tests
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"""
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import json
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from pathlib import Path
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from unittest import TestCase
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from unittest.mock import patch
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from metadata.generated.schema.api.data.createPipeline import CreatePipelineRequest
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from metadata.generated.schema.entity.data.pipeline import (
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Pipeline,
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PipelineStatus,
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StatusType,
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Task,
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TaskStatus,
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)
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from metadata.generated.schema.entity.services.pipelineService import (
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PipelineConnection,
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PipelineService,
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PipelineServiceType,
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)
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from metadata.generated.schema.metadataIngestion.workflow import (
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OpenMetadataWorkflowConfig,
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)
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from metadata.generated.schema.type.entityReference import EntityReference
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from metadata.ingestion.models.pipeline_status import OMetaPipelineStatus
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from metadata.ingestion.source.pipeline.databrickspipeline.metadata import (
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DatabrickspipelineSource,
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)
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from metadata.utils.logger import log_ansi_encoded_string
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mock_file_path = (
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Path(__file__).parent.parent.parent
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/ "resources/datasets/databricks_pipeline_resource.json"
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)
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with open(mock_file_path) as file:
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mock_data: dict = json.load(file)
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mock_file_path = (
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Path(__file__).parent.parent.parent
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/ "resources/datasets/databricks_pipeline_history.json"
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)
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with open(mock_file_path) as file:
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mock_history_data: dict = json.load(file)
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mock_databricks_config = {
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"source": {
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"type": "DatabricksPipeline",
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"serviceName": "DatabricksPipeline",
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"serviceConnection": {
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"config": {
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"type": "DatabricksPipeline",
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"token": "random_token",
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"hostPort": "localhost:443",
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"connectionArguments": {
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"http_path": "sql/1.0/endpoints/path",
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},
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}
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},
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"sourceConfig": {"config": {"type": "PipelineMetadata"}},
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},
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"sink": {"type": "metadata-rest", "config": {}},
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"workflowConfig": {
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"openMetadataServerConfig": {
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"hostPort": "http://localhost:8585/api",
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"authProvider": "openmetadata",
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"securityConfig": {
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"jwtToken": "eyJraWQiOiJHYjM4OWEtOWY3Ni1nZGpzLWE5MmotMDI0MmJrOTQzNTYiLCJ0eXAiOiJKV1QiLCJhbGc"
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"iOiJSUzI1NiJ9.eyJzdWIiOiJhZG1pbiIsImlzQm90IjpmYWxzZSwiaXNzIjoib3Blbi1tZXRhZGF0YS5vcmciLCJpYXQiOjE"
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"2NjM5Mzg0NjIsImVtYWlsIjoiYWRtaW5Ab3Blbm1ldGFkYXRhLm9yZyJ9.tS8um_5DKu7HgzGBzS1VTA5uUjKWOCU0B_j08WXB"
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"iEC0mr0zNREkqVfwFDD-d24HlNEbrqioLsBuFRiwIWKc1m_ZlVQbG7P36RUxhuv2vbSp80FKyNM-Tj93FDzq91jsyNmsQhyNv_fN"
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"r3TXfzzSPjHt8Go0FMMP66weoKMgW2PbXlhVKwEuXUHyakLLzewm9UMeQaEiRzhiTMU3UkLXcKbYEJJvfNFcLwSl9W8JCO_l0Yj3u"
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"d-qt_nQYEZwqW6u5nfdQllN133iikV4fM5QZsMCnm8Rq1mvLR0y9bmJiD7fwM1tmJ791TUWqmKaTnP49U493VanKpUAfzIiOiIbhg"
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},
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}
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},
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}
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MOCK_PIPELINE_SERVICE = PipelineService(
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id="85811038-099a-11ed-861d-0242ac120002",
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name="databricks_pipeline_test",
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connection=PipelineConnection(),
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serviceType=PipelineServiceType.DatabricksPipeline,
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)
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MOCK_PIPELINE = Pipeline(
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id="2aaa012e-099a-11ed-861d-0242ac120002",
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name="606358633757175",
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fullyQualifiedName="databricks_pipeline_source.606358633757175",
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displayName="OpenMetadata Databricks Workflow",
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tasks=[
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Task(
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name="task_1",
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displayName="task_1",
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taskType="notebook_task",
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downstreamTasks=["task_2", "task_3", "task_4"],
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),
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Task(
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name="task_2",
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displayName="task_2",
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taskType="spark_python_task",
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downstreamTasks=[],
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),
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Task(
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name="task_3",
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displayName="task_3",
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taskType="python_wheel_task",
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downstreamTasks=["task_5"],
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),
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Task(
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name="task_4",
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displayName="task_4",
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taskType="pipeline_task",
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downstreamTasks=["task_5"],
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),
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Task(
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name="task_5",
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displayName="task_5",
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taskType="sql_task",
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downstreamTasks=[],
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),
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],
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service=EntityReference(
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id="85811038-099a-11ed-861d-0242ac120002", type="pipelineService"
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),
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)
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EXPECTED_CREATED_PIPELINES = CreatePipelineRequest(
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name="606358633757175",
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displayName="OpenMetadata Databricks Workflow",
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description="OpenMetadata Databricks Workflow",
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tasks=[
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Task(
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name="task_1",
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displayName="task_1",
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taskType="notebook_task",
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downstreamTasks=["task_2", "task_3", "task_4"],
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),
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Task(
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name="task_2",
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displayName="task_2",
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taskType="spark_python_task",
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downstreamTasks=[],
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),
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Task(
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name="task_3",
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displayName="task_3",
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taskType="python_wheel_task",
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downstreamTasks=["task_5"],
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),
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Task(
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name="task_4",
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displayName="task_4",
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taskType="pipeline_task",
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downstreamTasks=["task_5"],
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),
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Task(
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name="task_5",
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displayName="task_5",
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taskType="sql_task",
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downstreamTasks=[],
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),
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],
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service=EntityReference(
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id="85811038-099a-11ed-861d-0242ac120002", type="pipelineService"
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),
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)
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EXPECTED_PIPELINE_STATUS = [
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OMetaPipelineStatus(
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pipeline_fqn="databricks_pipeline_source.606358633757175",
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pipeline_status=PipelineStatus(
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executionStatus=StatusType.Successful.value,
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taskStatus=[
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TaskStatus(
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name="one_task",
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executionStatus=StatusType.Successful.value,
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startTime=1672691730568,
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endTime=1672691793559,
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logLink="https://workspace.azuredatabricks.net/?o=workspace_id#job/325697581681107/run/821029",
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)
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],
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timestamp=1672691730552,
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),
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),
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OMetaPipelineStatus(
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pipeline_fqn="databricks_pipeline_source.606358633757175",
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pipeline_status=PipelineStatus(
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executionStatus=StatusType.Failed.value,
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taskStatus=[
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TaskStatus(
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name="one_task",
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executionStatus=StatusType.Failed.value,
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startTime=1672691610544,
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endTime=1672691677696,
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logLink="https://workspace.azuredatabricks.net/?o=workspace_id#job/325697581681107/run/820956",
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)
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],
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timestamp=1672691610525,
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),
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),
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]
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class DatabricksPipelineTests(TestCase):
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"""
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Implements the necessary methods to extract
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Databricks Pipeline test
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"""
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maxDiff = None
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@patch(
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"metadata.ingestion.source.pipeline.pipeline_service.PipelineServiceSource.test_connection"
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)
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def __init__(self, methodName, test_connection) -> None:
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super().__init__(methodName)
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log_ansi_encoded_string(message="init")
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test_connection.return_value = False
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config = OpenMetadataWorkflowConfig.parse_obj(mock_databricks_config)
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self.databricks = DatabrickspipelineSource.create(
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mock_databricks_config["source"],
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config.workflowConfig.openMetadataServerConfig,
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)
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self.databricks.context.__dict__["pipeline"] = MOCK_PIPELINE
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self.databricks.context.__dict__["pipeline_service"] = MOCK_PIPELINE_SERVICE
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self.databricks.context.__dict__["job_id_list"] = [
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mock_history_data[0]["job_id"]
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]
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@patch(
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"metadata.ingestion.source.database.databricks.client.DatabricksClient.list_jobs"
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)
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def test_get_pipelines_list(self, list_jobs):
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list_jobs.return_value = mock_data
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results = list(self.databricks.get_pipelines_list())
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self.assertEqual(mock_data, results)
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def test_yield_pipeline(self):
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pipelines = list(self.databricks.yield_pipeline(mock_data[0]))[0]
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self.assertEqual(pipelines, EXPECTED_CREATED_PIPELINES)
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@patch(
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"metadata.ingestion.source.database.databricks.client.DatabricksClient.get_job_runs"
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)
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def test_yield_pipeline_status(self, get_job_runs):
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get_job_runs.return_value = mock_history_data
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pipeline_status = list(
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self.databricks.yield_pipeline_status(mock_history_data[0]["job_id"])
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)
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self.assertEqual(pipeline_status, EXPECTED_PIPELINE_STATUS)
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