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cb72a22b59
* Fix - e2e tests for pydantic V2 * add correct default * add correct default * revert datetime aware * revert datetime aware * revert datetime aware * revert datetime aware * revert datetime aware * revert datetime aware * revert datetime aware * revert datetime aware * fix apis * format
114 lines
4.5 KiB
Python
114 lines
4.5 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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"""Test Ometa Dataframe utility tests"""
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import os
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import unittest
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from unittest.mock import patch
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import pyarrow.parquet as pq
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from metadata.generated.schema.entity.data.table import Table
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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.source.database.datalake.metadata import DatalakeSource
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from metadata.mixins.pandas.pandas_mixin import PandasInterfaceMixin
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from metadata.readers.dataframe.reader_factory import SupportedTypes
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from .topology.database.test_datalake import mock_datalake_config
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ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
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resp_parquet_file = (
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pq.ParquetFile(os.path.join(ROOT_DIR, "test_ometa_to_dataframe.parquet"))
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.read()
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.to_pandas()
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)
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method_resp_file = [resp_parquet_file]
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class TestStringMethods(unittest.TestCase):
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def test_dl_column_parser(self):
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with patch(
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"metadata.utils.datalake.datalake_utils.fetch_dataframe",
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return_value=method_resp_file,
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) as exec_mock_method:
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resp = exec_mock_method("key", "string")
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assert type(resp) == list
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@patch(
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"metadata.ingestion.source.database.database_service.DatabaseServiceSource.test_connection"
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)
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def test_return_ometa_dataframes_sampled(self, test_connection):
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with patch(
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"metadata.mixins.pandas.pandas_mixin.fetch_dataframe",
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return_value=[resp_parquet_file],
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):
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config = OpenMetadataWorkflowConfig.model_validate(mock_datalake_config)
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datalake_source = DatalakeSource.create(
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mock_datalake_config["source"],
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config.workflowConfig.openMetadataServerConfig,
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)
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resp = PandasInterfaceMixin().return_ometa_dataframes_sampled(
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service_connection_config=datalake_source.service_connection,
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table=Table(
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id="cec14ccf-123f-4271-8c90-0ae54cc4227e",
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columns=[],
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name="test",
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databaseSchema=EntityReference(
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name="Test",
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id="cec14ccf-123f-4271-8c90-0ae54cc4227e",
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type="databaseSchema",
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),
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fileFormat=SupportedTypes.PARQUET.value,
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),
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client=None,
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profile_sample_config=None,
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)
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assert resp == method_resp_file
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assert type(resp) == list
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@patch(
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"metadata.ingestion.source.database.database_service.DatabaseServiceSource.test_connection"
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)
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def test_return_ometa_dataframes_sampled_fail(self, test_connection):
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with patch(
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"metadata.mixins.pandas.pandas_mixin.fetch_dataframe",
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return_value=None,
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):
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with self.assertRaises(TypeError) as context:
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config = OpenMetadataWorkflowConfig.model_validate(mock_datalake_config)
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datalake_source = DatalakeSource.create(
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mock_datalake_config["source"],
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config.workflowConfig.openMetadataServerConfig,
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)
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resp = PandasInterfaceMixin().return_ometa_dataframes_sampled(
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service_connection_config=datalake_source.service_connection,
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table=Table(
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id="cec14ccf-123f-4271-8c90-0ae54cc4227e",
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columns=[],
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name="test",
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databaseSchema=EntityReference(
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name="Test",
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id="cec14ccf-123f-4271-8c90-0ae54cc4227e",
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type="databaseSchema",
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),
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fileFormat=None,
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),
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client=None,
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profile_sample_config=None,
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)
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self.assertEqual(context.exception.args[0], "Couldn't fetch test")
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