def test_expectation_configuration_get_evaluation_parameter_dependencies_with_query_store_formatted_urns(
):
ec = ExpectationConfiguration(
expectation_type="expect_column_values_to_be_in_set",
kwargs={
"column": "genre_id",
"value_set": {
"$PARAMETER":
"urn:great_expectations:stores:query_store:get_pet_names"
},
"result_format": "COMPLETE",
},
)
# Should fully skip `nested_update` calls in method due to lacking an "expectation_suite_name" key
dependencies = ec.get_evaluation_parameter_dependencies()
assert dependencies == {}
def test_expectation_configuration_get_evaluation_parameter_dependencies():
# Getting evaluation parameter dependencies relies on pyparsing, but the expectation
# configuration is responsible for ensuring that it only returns one copy of required metrics.
# If different expectations rely on the same upstream dependency,then it is possible for duplicates
# to be present nonetheless
ec = ExpectationConfiguration(
expectation_type="expect_column_values_to_be_between",
kwargs={
"column": "norm",
"min_value": {
"$PARAMETER":
"(-3 * urn:great_expectations:validations:profile:expect_column_stdev_to_be_between"
".result.observed_value:column=norm) + "
"urn:great_expectations:validations:profile:expect_column_mean_to_be_between.result.observed_value"
":column=norm"
},
"max_value": {
"$PARAMETER":
"(3 * urn:great_expectations:validations:profile:expect_column_stdev_to_be_between"
".result.observed_value:column=norm) + "
"urn:great_expectations:validations:profile:expect_column_mean_to_be_between.result.observed_value"
":column=norm"
},
},
)
dependencies = ec.get_evaluation_parameter_dependencies()
dependencies["profile"][0]["metric_kwargs_id"]["column=norm"] = set(
dependencies["profile"][0]["metric_kwargs_id"]["column=norm"])
assert {
"profile": [{
"metric_kwargs_id": {
"column=norm": {
"expect_column_stdev_to_be_between.result.observed_value",
"expect_column_mean_to_be_between.result.observed_value",
}
}
}]
} == dependencies
