update_matching_workflow ¶

def update_matching_workflow(
    self,
    workflow_name: "capo_entityresolution.types.entity_name.EntityName",
    input_source_config: "capo_entityresolution.types.input_source_config.InputSourceConfig",
    output_source_config: "capo_entityresolution.types.output_source_config.OutputSourceConfig",
    resolution_techniques: "capo_entityresolution.types.resolution_techniques.ResolutionTechniques",
    role_arn: str,
    *,
    config_overrides: Optional[EntityResolutionClientConfig] = None,
    description: Optional[
        "capo_entityresolution.types.description.Description"
    ] = None,
    incremental_run_config: Optional[
        "capo_entityresolution.types.incremental_run_config.IncrementalRunConfig"
    ] = None,
) -> "capo_entityresolution.types.update_matching_workflow_output.UpdateMatchingWorkflowOutput":
    """<p>Updates an existing matching workflow. The workflow must already exist for this operation to succeed.</p> <important> <p>For workflows where <code>resolutionType</code> is <code>PROVIDER</code>, incremental processing is not supported. </p> </important>

    Args:
        workflow_name: <p>The name of the workflow to be retrieved.</p>
        description: <p>A description of the workflow.</p>
        input_source_config: <p>A list of <code>InputSource</code> objects, which have the fields <code>InputSourceARN</code> and <code>SchemaName</code>.</p>
        output_source_config: <p>A list of <code>OutputSource</code> objects, each of which contains fields <code>outputS3Path</code>, <code>applyNormalization</code>, <code>KMSArn</code>, and <code>output</code>.</p>
        resolution_techniques: <p>An object which defines the <code>resolutionType</code> and the <code>ruleBasedProperties</code>.</p>
        incremental_run_config: <p>Optional. An object that defines the incremental run type. This object contains only the <code>incrementalRunType</code> field, which appears as "Automatic" in the console. </p> <important> <p>For workflows where <code>resolutionType</code> is <code>PROVIDER</code>, incremental processing is not supported. </p> </important>
        role_arn: <p>The Amazon Resource Name (ARN) of the IAM role. Entity Resolution assumes this role to create resources on your behalf as part of workflow execution.</p>

    Raises:
        capo_entityresolution.errors.access_denied_exception.AccessDeniedException: <p>You do not have sufficient access to perform this action. </p>
        capo_entityresolution.errors.internal_server_exception.InternalServerException: <p>This exception occurs when there is an internal failure in the Entity Resolution service. </p>
        capo_entityresolution.errors.resource_not_found_exception.ResourceNotFoundException: <p>The resource couldn't be found. </p>
        capo_entityresolution.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling. </p>
        capo_entityresolution.errors.validation_exception.ValidationException: <p>The input fails to satisfy the constraints specified by Entity Resolution. </p>
        capo_entityresolution.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_entityresolution.types.update_matching_workflow_input.UpdateMatchingWorkflowInput]",
    ) -> OperationResponse[
        "capo_entityresolution.types.update_matching_workflow_output.UpdateMatchingWorkflowOutput"
    ]:
        import capo_entityresolution._operations.aws_venice_service.update_matching_workflow

        output, http_response = (
            capo_entityresolution._operations.aws_venice_service.update_matching_workflow.update_matching_workflow(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("UpdateMatchingWorkflow")
    )
    input_: capo_entityresolution.types.update_matching_workflow_input.UpdateMatchingWorkflowInput = {
        "workflow_name": workflow_name,
        "input_source_config": input_source_config,
        "output_source_config": output_source_config,
        "resolution_techniques": resolution_techniques,
        "role_arn": role_arn,
    }
    if description is not None:
        input_["description"] = description
    if incremental_run_config is not None:
        input_["incremental_run_config"] = incremental_run_config

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output