b203b7d22c
Implement workflows for fake data generation, scouter processing, and core scouter operations - Added `FakeData` workflow to generate random data and send it to a Kafka topic. - Implemented `Scouter` workflow to load data from Kafka and trigger the core scouter workflow. - Created `CoreScouter` workflow to process data through quality gates, aggregation, and export to PostgreSQL. - Developed comprehensive unit tests for activities and workflows, ensuring proper functionality and error handling. - Enhanced Redis and Postgres activities with robust testing for data handling and error notifications. - Introduced quality filters for data validation and implemented tests to verify their functionality.
37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
from unittest.mock import MagicMock
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from pytest import fixture
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from sientia_do.notifications.models import Notification
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from scouter.activities.base import BaseActivity
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@fixture
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def base_activity():
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return BaseActivity(
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logger=MagicMock(),
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notification_handler=MagicMock(),
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)
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def test_prepare_activity(base_activity):
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base_activity.notification_handler.base_notification = Notification(
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project="project",
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pipeline="pipeline",
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trigger="-",
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model_name="-",
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model_id="-",
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)
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base_activity.prepare_activity(
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{
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'workflow_name': 'test_workflow',
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'schedule_name': 'test_schedule',
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'model_name': 'test_model',
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'model_id': 'test_model_id',
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}
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)
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assert base_activity.notification_handler.base_notification.schedule_name == "test_schedule"
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assert base_activity.notification_handler.base_notification.model_name == "test_model"
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assert base_activity.notification_handler.base_notification.model_id == "test_model_id"
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assert base_activity.notification_handler.base_notification.pipeline_name == "test_workflow"
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