vitor-aignosi
0e3ec6463f
Enhance E2E testing with MinIO support and update documentation - Updated `requirements-dev.txt` to include MinIO support in testcontainers. - Added a new fixture for MinIO container setup in `conftest.py` to facilitate E2E tests involving S3-compatible storage. - Introduced a new test fixture for activities using a real MinIO container in `conftest.py`. - Updated E2E test scenarios and documentation to reflect the integration of MinIO for offload uploads and clarified error handling in workflows. - Refactored existing tests to improve clarity and maintainability.
75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
"""
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Direct E2E execution of child workflows (smaller surface than PredictionsBatch).
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"""
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from decimal import Decimal
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import pytest
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from sqlalchemy import text
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from temporalio.testing import WorkflowEnvironment
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from temporalio.worker import Worker
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from e2e.helpers import make_workflow_id, start_and_await_workflow
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from laborious.workflows.sub_workflows.format_and_export_prediction import FormatAndExportPrediction
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@pytest.mark.asyncio
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@pytest.mark.integration
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async def test_format_and_export_prediction_default_path_e2e(
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temporal_test_env: WorkflowEnvironment,
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temporal_worker: Worker,
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postgres_engine,
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):
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"""
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Run FormatAndExportPrediction with path_flag set (format_default_prediction path).
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"""
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client = temporal_test_env.client
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model_id = 401
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with postgres_engine.begin() as conn:
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conn.execute(text(f'DELETE FROM predictions_schema.predictions WHERE model_id = {model_id}'))
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metadata = {
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'metadata': {
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'model_id': model_id,
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'model_name': 'test_model',
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'schedule_name': 'test-schedule',
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'workflow_name': 'subworkflow.format_and_export_prediction',
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}
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}
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input_data = {
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'metadata': metadata,
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'path_flag': 'CONTINUE',
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'data': {'last_timestamp': '2024-01-01 12:00:00+00:00'},
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'prediction_confidence': 2,
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'timestamp': '2024-01-01 12:00:00+00:00',
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'model_id': model_id,
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'model_name': 'test_model',
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'schema': 'predictions_schema',
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'table_name': 'predictions',
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'transform_table_name': 'transformed_data',
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'comment': 'e2e child workflow default path',
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'opc_output_config': {},
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'pi_web_api_output_config': {},
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'prediction_store_policy': 'lts:1',
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}
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await start_and_await_workflow(
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client,
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FormatAndExportPrediction.run,
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input_data,
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make_workflow_id('e2e-format-export-child'),
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)
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with postgres_engine.connect() as conn:
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row = conn.execute(
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text(
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f'SELECT prediction, prediction_confidence, prediction_status, comments '
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f'FROM predictions_schema.predictions WHERE model_id = {model_id}'
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)
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).fetchone()
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assert row is not None
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assert row[0] == 0
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assert row[1] == Decimal(2)
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assert row[2] == 'Bad'
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assert row[3] == 'e2e child workflow default path'
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