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put_workflow_run_properties

put_workflow_run_properties(
    name: NameString,
    run_id: IdString,
    run_properties: WorkflowRunProperties,
    *,
    config_overrides: Optional[GlueClientConfig] = None,
) -> PutWorkflowRunPropertiesResponse

Puts the specified workflow run properties for the given workflow run. If a property already exists for the specified run, then it overrides the value otherwise adds the property to existing properties.

Parameters:

  • name (NameString) –

    Name of the workflow which was run.

  • run_id (IdString) –

    The ID of the workflow run for which the run properties should be updated.

  • run_properties (WorkflowRunProperties) –

    The properties to put for the specified run.

    Run properties may be logged. Do not pass plaintext secrets as properties. Retrieve secrets from a Glue Connection, Amazon Web Services Secrets Manager or other secret management mechanism if you intend to use them within the workflow run.

Raises:

Source code in docs_src/capo_glue/_services/glue.py
def put_workflow_run_properties(
    self,
    name: "capo_glue.types.name_string.NameString",
    run_id: "capo_glue.types.id_string.IdString",
    run_properties: "capo_glue.types.workflow_run_properties.WorkflowRunProperties",
    *,
    config_overrides: Optional[GlueClientConfig] = None,
) -> "capo_glue.types.put_workflow_run_properties_response.PutWorkflowRunPropertiesResponse":
    """<p>Puts the specified workflow run properties for the given workflow run. If a property already exists for the specified run, then it overrides the value otherwise adds the property to existing properties.</p>

    Args:
        name: <p>Name of the workflow which was run.</p>
        run_id: <p>The ID of the workflow run for which the run properties should be updated.</p>
        run_properties: <p>The properties to put for the specified run.</p> <p>Run properties may be logged. Do not pass plaintext secrets as properties. Retrieve secrets from a Glue Connection, Amazon Web Services Secrets Manager or other secret management mechanism if you intend to use them within the workflow run.</p>

    Raises:
        capo_glue.errors.already_exists_exception.AlreadyExistsException: <p>A resource to be created or added already exists.</p>
        capo_glue.errors.concurrent_modification_exception.ConcurrentModificationException: <p>Two processes are trying to modify a resource simultaneously.</p>
        capo_glue.errors.entity_not_found_exception.EntityNotFoundException: <p>A specified entity does not exist</p>
        capo_glue.errors.internal_service_exception.InternalServiceException: <p>An internal service error occurred.</p>
        capo_glue.errors.invalid_input_exception.InvalidInputException: <p>The input provided was not valid.</p>
        capo_glue.errors.operation_timeout_exception.OperationTimeoutException: <p>The operation timed out.</p>
        capo_glue.errors.resource_number_limit_exceeded_exception.ResourceNumberLimitExceededException: <p>A resource numerical limit was exceeded.</p>
        capo_glue.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_glue.types.put_workflow_run_properties_request.PutWorkflowRunPropertiesRequest]",
    ) -> OperationResponse[
        "capo_glue.types.put_workflow_run_properties_response.PutWorkflowRunPropertiesResponse"
    ]:
        import capo_glue._operations.aws_glue.put_workflow_run_properties

        output, http_response = (
            capo_glue._operations.aws_glue.put_workflow_run_properties.put_workflow_run_properties(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("PutWorkflowRunProperties")
    )
    input_: capo_glue.types.put_workflow_run_properties_request.PutWorkflowRunPropertiesRequest = {
        "name": name,
        "run_id": run_id,
        "run_properties": run_properties,
    }

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