mirror of
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217 lines
8.4 KiB
Python
217 lines
8.4 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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Test Deltalake using the topology
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Here we don't need to patch, as we can just create our own metastore
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"""
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import shutil
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from datetime import date, datetime
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from unittest import TestCase
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from metadata.generated.schema.api.data.createDatabase import CreateDatabaseRequest
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from metadata.generated.schema.api.data.createDatabaseSchema import (
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CreateDatabaseSchemaRequest,
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)
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from metadata.generated.schema.api.data.createTable import CreateTableRequest
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from metadata.generated.schema.entity.data.database import Database
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from metadata.generated.schema.entity.data.databaseSchema import DatabaseSchema
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from metadata.generated.schema.entity.data.table import Column, DataType, TableType
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from metadata.generated.schema.entity.services.databaseService import (
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DatabaseConnection,
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DatabaseService,
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DatabaseServiceType,
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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.source.database.deltalake.metadata import DeltalakeSource
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METASTORE_PATH = "/tmp/spark/unit/metastore"
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SPARK_SQL_WAREHOUSE = "/tmp/spark/unit/warehouse"
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MOCK_DELTA_CONFIG = {
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"source": {
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"type": "deltalake",
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"serviceName": "delta",
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"serviceConnection": {
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"config": {
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"type": "DeltaLake",
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"metastoreConnection": {
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"metastoreFilePath": METASTORE_PATH,
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},
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"connectionArguments": {
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"spark.sql.warehouse.dir": SPARK_SQL_WAREHOUSE,
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},
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}
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},
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"sourceConfig": {"config": {"type": "DatabaseMetadata"}},
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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": "eyJraWQiOiJHYjM4OWEtOWY3Ni1nZGpzLWE5MmotMDI0MmJrOTQzNTYiLCJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJzdWIiOiJhZG1pbiIsImlzQm90IjpmYWxzZSwiaXNzIjoib3Blbi1tZXRhZGF0YS5vcmciLCJpYXQiOjE2NjM5Mzg0NjIsImVtYWlsIjoiYWRtaW5Ab3Blbm1ldGFkYXRhLm9yZyJ9.tS8um_5DKu7HgzGBzS1VTA5uUjKWOCU0B_j08WXBiEC0mr0zNREkqVfwFDD-d24HlNEbrqioLsBuFRiwIWKc1m_ZlVQbG7P36RUxhuv2vbSp80FKyNM-Tj93FDzq91jsyNmsQhyNv_fNr3TXfzzSPjHt8Go0FMMP66weoKMgW2PbXlhVKwEuXUHyakLLzewm9UMeQaEiRzhiTMU3UkLXcKbYEJJvfNFcLwSl9W8JCO_l0Yj3ud-qt_nQYEZwqW6u5nfdQllN133iikV4fM5QZsMCnm8Rq1mvLR0y9bmJiD7fwM1tmJ791TUWqmKaTnP49U493VanKpUAfzIiOiIbhg"
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},
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}
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},
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}
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MOCK_DATABASE_SERVICE = DatabaseService(
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id="85811038-099a-11ed-861d-0242ac120002",
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name="delta",
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connection=DatabaseConnection(),
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serviceType=DatabaseServiceType.DeltaLake,
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)
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MOCK_DATABASE = Database(
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id="2004514B-A800-4D92-8442-14B2796F712E",
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name="default",
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fullyQualifiedName="delta.default",
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service=EntityReference(
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id="85811038-099a-11ed-861d-0242ac120002", type="databaseService"
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),
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)
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MOCK_DATABASE_SCHEMA = DatabaseSchema(
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id="92D36A9B-B1A9-4D0A-A00B-1B2ED137ABA5",
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name="default",
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fullyQualifiedName="delta.default.default",
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database=EntityReference(
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id="2004514B-A800-4D92-8442-14B2796F712E", type="database"
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),
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service=EntityReference(
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id="85811038-099a-11ed-861d-0242ac120002", type="databaseService"
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),
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)
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class DeltaLakeUnitTest(TestCase):
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"""
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Add method validations from Deltalake ingestion
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"""
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config: OpenMetadataWorkflowConfig = OpenMetadataWorkflowConfig.parse_obj(
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MOCK_DELTA_CONFIG
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)
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delta: DeltalakeSource = DeltalakeSource.create(
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MOCK_DELTA_CONFIG["source"], config.workflowConfig.openMetadataServerConfig
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)
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spark = delta.spark
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@classmethod
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def setUpClass(cls) -> None:
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"""
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Prepare the SparkSession and metastore
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"""
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df = cls.spark.createDataFrame(
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[
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(1, 2.0, "string1", date(2000, 1, 1), datetime(2000, 1, 1, 12, 0)),
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(2, 3.0, "string2", date(2000, 2, 1), datetime(2000, 1, 2, 12, 0)),
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(3, 4.0, "string3", date(2000, 3, 1), datetime(2000, 1, 3, 12, 0)),
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],
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schema="a long, b double, c string, d date, e timestamp",
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)
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# Create the DF as a tmp view to be able to run Spark SQL statements on top
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df.createOrReplaceTempView("tmp_df")
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# If no db is specified, the table will be created under `default`
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cls.spark.sql(
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"CREATE TABLE IF NOT EXISTS my_df COMMENT 'testing around' AS SELECT * FROM tmp_df"
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)
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# Create a database. We will be ingesting that as a schema
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cls.spark.sql(
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f"CREATE DATABASE sample_db LOCATION '{SPARK_SQL_WAREHOUSE}/sample_db'"
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)
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# Set context
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cls.delta.context.__dict__["database_service"] = MOCK_DATABASE_SERVICE
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cls.delta.context.__dict__["database"] = MOCK_DATABASE
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cls.delta.context.__dict__["database_schema"] = MOCK_DATABASE_SCHEMA
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# We pick up the table comments when getting their name and type, so we
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# store the description in the context
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cls.delta.context.__dict__["table_description"] = "testing around"
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@classmethod
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def tearDownClass(cls) -> None:
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"""
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Clean up
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"""
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shutil.rmtree(METASTORE_PATH)
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shutil.rmtree(SPARK_SQL_WAREHOUSE)
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def test_get_database_names(self):
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database_names = list(self.delta.get_database_names())
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self.assertEqual(database_names, ["default"])
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def test_yield_database(self):
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database_requests = list(self.delta.yield_database(database_name="default"))
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expected_database_request = CreateDatabaseRequest(
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name="default",
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service=EntityReference(
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id="85811038-099a-11ed-861d-0242ac120002", type="databaseService"
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),
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)
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self.assertEqual(database_requests, [expected_database_request])
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def test_get_database_schema_names(self):
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schema_names = set(self.delta.get_database_schema_names())
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self.assertEqual(schema_names, {"default", "sample_db"})
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def test_yield_database_schema(self):
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schema_requests = list(self.delta.yield_database_schema(schema_name="default"))
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expected_schema_request = CreateDatabaseSchemaRequest(
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name="default",
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database=EntityReference(
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id="2004514B-A800-4D92-8442-14B2796F712E", type="database"
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),
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)
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self.assertEqual(schema_requests, [expected_schema_request])
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def test_get_tables_name_and_type(self):
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table_names = list(self.delta.get_tables_name_and_type())
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# We won't ingest TMP tables
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self.assertEqual(table_names, [("my_df", TableType.Regular)])
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def test_yield_table(self):
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table_requests = list(
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self.delta.yield_table(table_name_and_type=("my_df", TableType.Regular))
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)
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expected_columns = [
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Column(name="a", dataType=DataType.BIGINT, dataTypeDisplay="bigint"),
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Column(name="b", dataType=DataType.DOUBLE, dataTypeDisplay="double"),
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Column(name="c", dataType=DataType.STRING, dataTypeDisplay="string"),
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Column(name="d", dataType=DataType.DATE, dataTypeDisplay="date"),
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Column(name="e", dataType=DataType.TIMESTAMP, dataTypeDisplay="timestamp"),
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]
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expected_table_request = CreateTableRequest(
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name="my_df",
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tableType=TableType.Regular,
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description="testing around",
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columns=expected_columns,
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tableConstraints=None,
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databaseSchema=EntityReference(
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id="92D36A9B-B1A9-4D0A-A00B-1B2ED137ABA5",
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type="databaseSchema",
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),
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viewDefinition=None,
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)
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self.assertEqual(table_requests, [expected_table_request])
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