def test_instantiation_via_url_and_retrieve_data_with_other_dialect(sa):
"""Ensure that we can still retrieve data when the dialect is not recognized."""
# 1. Create engine with sqlite db
db_file = file_relative_path(
__file__,
os.path.join("..", "test_sets",
"test_cases_for_sql_data_connector.db"),
)
my_execution_engine = SqlAlchemyExecutionEngine(url="sqlite:///" + db_file)
assert my_execution_engine.connection_string is None
assert my_execution_engine.credentials is None
assert my_execution_engine.url[
-36:] == "test_cases_for_sql_data_connector.db"
# 2. Change dialect to one not listed in GESqlDialect
my_execution_engine.engine.dialect.name = "other_dialect"
# 3. Get data
num_rows_in_sample: int = 10
batch_data, _ = my_execution_engine.get_batch_data_and_markers(
batch_spec=SqlAlchemyDatasourceBatchSpec(
table_name="table_partitioned_by_date_column__A",
sampling_method="_sample_using_limit",
sampling_kwargs={"n": num_rows_in_sample},
))
# 4. Assert dialect and data are as expected
assert batch_data.dialect == GESqlDialect.OTHER
my_execution_engine.load_batch_data("__", batch_data)
validator = Validator(my_execution_engine)
assert len(validator.head(fetch_all=True)) == num_rows_in_sample
def _build_sa_engine(df, sa):
eng = sa.create_engine("sqlite://", echo=False)
df.to_sql("test", eng, index=False)
engine = SqlAlchemyExecutionEngine(engine=eng)
batch_data = SqlAlchemyBatchData(execution_engine=engine, table_name="test")
engine.load_batch_data("my_id", batch_data)
return engine
def test_sa_expect_column_value_z_scores_to_be_less_than_impl(postgresql_engine):
df = pd.DataFrame({"a": [1, 5, 22, 3, 5, 10]})
df.to_sql(
name="z_score_test_data",
con=postgresql_engine,
index=False,
if_exists="replace",
)
expectationConfiguration = ExpectationConfiguration(
expectation_type="expect_column_value_z_scores_to_be_less_than",
kwargs={
"column": "a",
"mostly": 0.9,
"threshold": 4,
"double_sided": True,
},
)
expectation = ExpectColumnValueZScoresToBeLessThan(expectationConfiguration)
engine = SqlAlchemyExecutionEngine(engine=postgresql_engine)
engine.load_batch_data(
"my_id",
SqlAlchemyBatchData(execution_engine=engine, table_name="z_score_test_data"),
)
result = expectation.validate(Validator(execution_engine=engine))
assert result == ExpectationValidationResult(
success=True,
)