Checkpoint
class great_expectations.Checkpoint(*, name: str, validation_definitions: List[great_expectations.core.validation_definition.ValidationDefinition], actions: List[great_expectations.checkpoint.actions.ValidationAction] = None, result_format: Union[great_expectations.core.result_format.ResultFormat, dict, Literal['BOOLEAN_ONLY', 'BASIC', 'SUMMARY', 'COMPLETE']] = ResultFormat.SUMMARY, id: Optional[str] = None)#
A Checkpoint is the primary means for validating data in a production deployment of Great Expectations.
Checkpoints provide a convenient abstraction for running a number of validation definitions and triggering a set of actions to be taken after the validation step.
- Parameters
name – The name of the checkpoint.
validation_definitions – List of validation definitions to be run.
actions – List of actions to be taken after the validation definitions are run.
result_format – The format in which to return the results of the validation definitions. Default is ResultFormat.SUMMARY.
id – An optional unique identifier for the checkpoint.
run(batch_parameters: Dict[str, Any] | None = None, expectation_parameters: SuiteParameterDict | None = None, run_id: RunIdentifier | None = None) CheckpointResult#
Runs the Checkpoint’s underlying Validation Definitions and Actions.
- Parameters
batch_parameters – Parameters to be used when loading the Batch.
expectation_parameters – Parameters to be used when validating the Batch.
run_id – An optional unique identifier for the run.
- Returns
A CheckpointResult object containing the results of the run.
- Raises
CheckpointRunWithoutValidationDefinitionError – If the Checkpoint is run without any Validation Definitions.
CheckpointNotAddedError – If the Checkpoint has not been added to the Store.
CheckpointNotFreshError – If the Checkpoint has been modified since it was last added to the Store.
- save() None#
Save the current state of this Checkpoint.