def test_column_fallback(sa):
engine = sa.create_engine("sqlite://")
data = pd.DataFrame({
"name": ["Frank", "Steve", "Jane", "Frank", "Michael"],
"age": [16, 21, 38, 22, 10],
"pet": ["fish", "python", "cat", "python", "frog"],
})
data.to_sql(name="test_sql_data", con=engine, index=False)
dataset = SqlAlchemyDataset("test_sql_data", engine=engine)
assert set(dataset.get_table_columns()) == {"name", "age", "pet"}
fallback_dataset = SqlAlchemyDataset("test_sql_data", engine=engine)
# override columns attribute to test fallback
fallback_dataset.columns = fallback_dataset.column_reflection_fallback()
assert set(fallback_dataset.get_table_columns()) == {"name", "age", "pet"}
# check that the results are the same for a few expectations
assert dataset.expect_column_to_exist(
"age") == fallback_dataset.expect_column_to_exist("age")
assert dataset.expect_column_mean_to_be_between(
"age",
min_value=10) == fallback_dataset.expect_column_mean_to_be_between(
"age", min_value=10)
# Test a failing expectation
assert dataset.expect_table_row_count_to_equal(
value=3) == fallback_dataset.expect_table_row_count_to_equal(value=3)
def test_sqlalchemy_dataset_view(sqlite_view_engine):
# This test demonstrates that a view can be used as a SqlAlchemyDataset table for purposes of validation
dataset = SqlAlchemyDataset("test_view", engine=sqlite_view_engine)
res = dataset.expect_table_row_count_to_equal(1)
assert res.success is True
# A temp view can also be used, though generators will not see it
dataset = SqlAlchemyDataset("test_temp_view", engine=sqlite_view_engine)
res = dataset.expect_table_row_count_to_equal(3)
assert res.success is True