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.
30 lines
744 B
Python
30 lines
744 B
Python
from unittest.mock import AsyncMock, patch, ANY
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from pytest import fixture, mark
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from scouter.workflow.fake_data import FakeData
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from scouter.activities.faker import Faker
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@fixture
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def fake_data():
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return FakeData()
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@mark.asyncio
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@patch('scouter.workflow.fake_data.workflow', new_callable=AsyncMock)
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async def test_fake_data_workflow(mock_workflow, fake_data):
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mock_workflow.execute_activity_method.return_value = None
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await fake_data.run(
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{
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'topic': 'test_topic'
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}
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)
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mock_workflow.execute_activity_method.assert_called_once_with(
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Faker.generate_and_send_data,
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{
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'topic': 'test_topic'
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},
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retry_policy=ANY,
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start_to_close_timeout=ANY
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)
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