def test_sampling_method__limit(
test_cases_for_sql_data_connector_sqlite_execution_engine,
):
execution_engine = test_cases_for_sql_data_connector_sqlite_execution_engine
batch_data, batch_markers = execution_engine.get_batch_data_and_markers(
batch_spec=SqlAlchemyDatasourceBatchSpec(
{
"table_name": "table_partitioned_by_date_column__A",
"batch_identifiers": {},
"splitter_method": "_split_on_whole_table",
"splitter_kwargs": {},
"sampling_method": "_sample_using_limit",
"sampling_kwargs": {"n": 20},
}
)
)
batch = Batch(data=batch_data)
validator = Validator(execution_engine, batches=[batch])
assert len(validator.head(fetch_all=True)) == 20
assert not validator.expect_column_values_to_be_in_set(
"date", value_set=["2020-01-02"]
).success
def test_sampling_method__limit(
test_cases_for_sql_data_connector_sqlite_execution_engine,
):
execution_engine = test_cases_for_sql_data_connector_sqlite_execution_engine
batch_data, batch_markers = execution_engine.get_batch_data_and_markers(
batch_spec=BatchSpec(
{
"table_name": "table_partitioned_by_date_column__A",
"partition_definition": {},
"splitter_method": "_split_on_whole_table",
"splitter_kwargs": {},
"sampling_method": "_sample_using_limit",
"sampling_kwargs": {"n": 20},
}
)
)
execution_engine.load_batch_data("__", batch_data)
validator = Validator(execution_engine)
assert len(validator.head(fetch_all=True)) == 20
assert (
validator.expect_column_values_to_be_in_set(
"date", value_set=["2020-01-02"]
).success
== False
)
def test_sqlalchemy_source_templating(sqlitedb_engine):
datasource = SqlAlchemyDatasource(engine=sqlitedb_engine, generators={
"foo": {
"class_name": "QueryBatchKwargsGenerator"
}
})
generator = datasource.get_generator("foo")
generator.add_query("test", "select 'cat' as ${col_name};")
batch = datasource.get_batch(generator.build_batch_kwargs("test", query_parameters={'col_name': "animal_name"}))
dataset = Validator(batch, expectation_suite=ExpectationSuite("test"), expectation_engine=SqlAlchemyDataset).get_dataset()
res = dataset.expect_column_to_exist("animal_name")
assert res.success is True
res = dataset.expect_column_values_to_be_in_set('animal_name', ['cat'])
assert res.success is True