get_workflow ¶

get_workflow(
    workflow_arn: WorkflowArn,
    *,
    config_overrides: Optional[MWAAServerlessClientConfig] = None,
    workflow_version: Optional[WorkflowVersion] = None,
) -> GetWorkflowResponse

Retrieves detailed information about a workflow, including its configuration, status, and metadata.

Parameters:

workflow_arn (WorkflowArn) –

The Amazon Resource Name (ARN) of the workflow you want to retrieve.
workflow_version (Optional[WorkflowVersion], default: None ) –

Optional. The specific version of the workflow to retrieve. If not specified, the latest version is returned.

Raises:

AccessDeniedException –

You do not have sufficient permission to perform this action.
InternalServerException –

An unexpected server-side error occurred during request processing.
OperationTimeoutException –

The operation timed out.
ResourceNotFoundException –

The specified resource was not found. You can only access or modify a resource that already exists.
ThrottlingException –

The request was denied because too many requests were made in a short period, exceeding the service rate limits. Amazon Managed Workflows for Apache Airflow Serverless implements throttling controls to ensure fair resource allocation across all customers in the multi-tenant environment. This helps maintain service stability and performance. If you encounter throttling, implement exponential backoff and retry logic in your applications, or consider distributing your API calls over a longer time period.
ValidationException –

The specified request parameters are invalid, missing, or inconsistent with Amazon Managed Workflows for Apache Airflow Serverless service requirements. This can occur when workflow definitions contain unsupported operators, when required IAM permissions are missing, when S3 locations are inaccessible, or when network configurations are invalid. The service validates workflow definitions, execution roles, and resource configurations to ensure compatibility with the managed Airflow environment and security requirements.
UnknownServiceError –

The service returned an error code this client does not model.

Source code in docs_src/capo_mwaa_serverless/_services/mwaa_serverless.py

def get_workflow(
    self,
    workflow_arn: "capo_mwaa_serverless.types.workflow_arn.WorkflowArn",
    *,
    config_overrides: Optional[MWAAServerlessClientConfig] = None,
    workflow_version: Optional[
        "capo_mwaa_serverless.types.workflow_version.WorkflowVersion"
    ] = None,
) -> "capo_mwaa_serverless.types.get_workflow_response.GetWorkflowResponse":
    """<p>Retrieves detailed information about a workflow, including its configuration, status, and metadata.</p>

    Args:
        workflow_arn: <p>The Amazon Resource Name (ARN) of the workflow you want to retrieve.</p>
        workflow_version: <p>Optional. The specific version of the workflow to retrieve. If not specified, the latest version is returned.</p>

    Raises:
        capo_mwaa_serverless.errors.access_denied_exception.AccessDeniedException: <p>You do not have sufficient permission to perform this action.</p>
        capo_mwaa_serverless.errors.internal_server_exception.InternalServerException: <p>An unexpected server-side error occurred during request processing.</p>
        capo_mwaa_serverless.errors.operation_timeout_exception.OperationTimeoutException: <p>The operation timed out.</p>
        capo_mwaa_serverless.errors.resource_not_found_exception.ResourceNotFoundException: <p>The specified resource was not found. You can only access or modify a resource that already exists.</p>
        capo_mwaa_serverless.errors.throttling_exception.ThrottlingException: <p>The request was denied because too many requests were made in a short period, exceeding the service rate limits. Amazon Managed Workflows for Apache Airflow Serverless implements throttling controls to ensure fair resource allocation across all customers in the multi-tenant environment. This helps maintain service stability and performance. If you encounter throttling, implement exponential backoff and retry logic in your applications, or consider distributing your API calls over a longer time period.</p>
        capo_mwaa_serverless.errors.validation_exception.ValidationException: <p>The specified request parameters are invalid, missing, or inconsistent with Amazon Managed Workflows for Apache Airflow Serverless service requirements. This can occur when workflow definitions contain unsupported operators, when required IAM permissions are missing, when S3 locations are inaccessible, or when network configurations are invalid. The service validates workflow definitions, execution roles, and resource configurations to ensure compatibility with the managed Airflow environment and security requirements.</p>
        capo_mwaa_serverless.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_mwaa_serverless.types.get_workflow_request.GetWorkflowRequest]",
    ) -> OperationResponse[
        "capo_mwaa_serverless.types.get_workflow_response.GetWorkflowResponse"
    ]:
        import capo_mwaa_serverless._operations.amazon_mwaa_serverless.get_workflow

        output, http_response = (
            capo_mwaa_serverless._operations.amazon_mwaa_serverless.get_workflow.get_workflow(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("GetWorkflow")
    )
    input_: capo_mwaa_serverless.types.get_workflow_request.GetWorkflowRequest = {
        "workflow_arn": workflow_arn
    }
    if workflow_version is not None:
        input_["workflow_version"] = workflow_version

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