QueryExpectation
- class great_expectations.expectations.expectation.QueryExpectation(configuration: Optional[great_expectations.core.expectation_configuration.ExpectationConfiguration] = None)#
Base class for QueryExpectations.
QueryExpectations facilitate the execution of SQL or Spark-SQL queries as the core logic for an Expectation.
QueryExpectations must implement a _validate(…) method containing logic for determining whether data returned by the executed query is successfully validated.
Query Expectations may optionally provide implementations of:
validate_configuration, which should raise an error if the configuration will not be usable for the Expectation.
Data Docs rendering methods decorated with the @renderer decorator.
QueryExpectations may optionally define a query attribute, and specify that query as a default in default_kwarg_values.
Doing so precludes the need to pass a query into the Expectation. This default will be overridden if a query is passed in.
- Parameters
domain_keys (tuple) – A tuple of the keys used to determine the domain of the expectation.
success_keys (tuple) – A tuple of the keys used to determine the success of the expectation.
runtime_keys (optional[tuple]) – Optional. A tuple of the keys that can be used to control output but will not affect the actual success value of the expectation (such as result_format).
default_kwarg_values (optional[dict]) – Optional. A dictionary that will be used to fill unspecified kwargs from the Expectation Configuration.
query (optional[str]) – Optional. A SQL or Spark-SQL query to be executed. If not provided, a query must be passed into the QueryExpectation.
- Relevant Documentation Links
- domain_type = 'table'#
- get_success_kwargs(configuration: Optional[great_expectations.core.expectation_configuration.ExpectationConfiguration] = None) Dict[str, Any]#
Retrieve the success kwargs.
- Parameters
configuration – The ExpectationConfiguration that contains the kwargs. If no configuration arg is provided, the success kwargs from the configuration attribute of the Expectation instance will be returned.
- print_diagnostic_checklist(diagnostics: Optional[great_expectations.core.expectation_diagnostics.expectation_diagnostics.ExpectationDiagnostics] = None, show_failed_tests: bool = False) str#
Runs self.run_diagnostics and generates a diagnostic checklist.
This output from this method is a thin wrapper for ExpectationDiagnostics.generate_checklist() This method is experimental.
- Parameters
diagnostics (optional[ExpectationDiagnostics]) – If diagnostics are not provided, diagnostics will be ran on self.
show_failed_tests (bool) – If true, failing tests will be printed.
- run_diagnostics(raise_exceptions_for_backends: bool = False, ignore_suppress: bool = False, ignore_only_for: bool = False, for_gallery: bool = False, debug_logger: Optional[logging.Logger] = None, only_consider_these_backends: Optional[List[str]] = None, context: Optional[AbstractDataContext] = None) ExpectationDiagnostics#
Produce a diagnostic report about this Expectation.
The current uses for this method’s output are using the JSON structure to populate the Public Expectation Gallery and enabling a fast dev loop for developing new Expectations where the contributors can quickly check the completeness of their expectations.
The contents of the report are captured in the ExpectationDiagnostics dataclass. You can see some examples in test_expectation_diagnostics.py
Some components (e.g. description, examples, library_metadata) of the diagnostic report can be introspected directly from the Exepctation class. Other components (e.g. metrics, renderers, executions) are at least partly dependent on instantiating, validating, and/or executing the Expectation class. For these kinds of components, at least one test case with include_in_gallery=True must be present in the examples to produce the metrics, renderers and execution engines parts of the report. This is due to a get_validation_dependencies requiring expectation_config as an argument.
If errors are encountered in the process of running the diagnostics, they are assumed to be due to incompleteness of the Expectation’s implementation (e.g., declaring a dependency on Metrics that do not exist). These errors are added under “errors” key in the report.
- Parameters
raise_exceptions_for_backends – Bool object that when True will raise an Exception if a backend fails to connect.
ignore_suppress – Bool object that when True will ignore the suppress_test_for list on Expectation sample tests.
ignore_only_for – Bool object that when True will ignore the only_for list on Expectation sample tests.
for_gallery – Bool object that when True will create empty arrays to use as examples for the Expectation Diagnostics.
debug_logger (optional[logging.Logger]) – Logger object to use for sending debug messages to.
only_consider_these_backends (optional[List[str]]) –
context (optional[AbstractDataContext]) – Instance of any child of “AbstractDataContext” class.
- Returns
An Expectation Diagnostics report object
- validate(validator: Validator, configuration: Optional[ExpectationConfiguration] = None, evaluation_parameters: Optional[dict] = None, interactive_evaluation: bool = True, data_context: Optional[AbstractDataContext] = None, runtime_configuration: Optional[dict] = None) ExpectationValidationResult#
Validates the expectation against the provided data.
- Parameters
validator – A Validator object that can be used to create Expectations, validate Expectations, and get Metrics for Expectations.
configuration – Defines the parameters and name of a specific expectation.
evaluation_parameters – Dictionary of dynamic values used during Validation of an Expectation.
interactive_evaluation – Setting the interactive_evaluation flag on a DataAsset make it possible to declare expectations and store expectations without immediately evaluating them.
data_context – An instance of a GX DataContext.
runtime_configuration – The runtime configuration for the Expectation.
- Returns
An ExpectationValidationResult object