start_session ¶

def start_session(
    self,
    *,
    config_overrides: Optional[EMRClientConfig] = None,
    name: Optional[
        "capo_emr.types.xml_string_max_len256.XmlStringMaxLen256"
    ] = None,
    cluster_id: Optional["capo_emr.types.cluster_id.ClusterId"] = None,
    execution_role_arn: Optional["capo_emr.types.iam_role_arn.IAMRoleArn"] = None,
    engine_configurations: Optional[
        "capo_emr.types.configuration_list.ConfigurationList"
    ] = None,
    monitoring_configuration: Optional[
        "capo_emr.types.session_monitoring_configuration.SessionMonitoringConfiguration"
    ] = None,
    session_idle_timeout_in_minutes: Optional["capo_emr.types.long.Long"] = None,
    client_request_token: Optional[
        "capo_emr.types.client_request_token.ClientRequestToken"
    ] = None,
    tags: Optional["capo_emr.types.tag_list.TagList"] = None,
) -> "capo_emr.types.start_session_output.StartSessionOutput":
    """<p>Creates and starts a new Spark Connect session on the specified cluster. The cluster must be in the <code>RUNNING</code> or <code>WAITING</code> state and have sessions enabled. This operation is supported in Amazon EMR Spark 8.0.0 and later.</p>

    Args:
        name: <p>An optional name for the session.</p>
        cluster_id: <p>The ID of the cluster on which to start the session.</p>
        execution_role_arn: <p>The execution role ARN for the session. Amazon EMR uses this role to access Amazon Web Services resources on your behalf during session execution.</p>
        engine_configurations: <p>The configuration overrides for the session. Only runtime configuration overrides are supported.</p>
        monitoring_configuration: <p>The monitoring configuration that controls where session logs are published, such as Amazon S3, CloudWatch, or managed logging.</p>
        session_idle_timeout_in_minutes: <p>The idle timeout, in minutes. If the session is idle for this duration, Amazon EMR EC2 automatically terminates it.</p>
        client_request_token: <p>A unique, case-sensitive identifier that you provide to ensure the idempotency of the request. If you retry a request that completed successfully using the same client request token, the service returns the original response without performing the operation again.</p>
        tags: <p>The tags to assign to the session.</p>

    Raises:
        capo_emr.errors.internal_server_exception.InternalServerException: <p>This exception occurs when there is an internal failure in the Amazon EMR service.</p>
        capo_emr.errors.invalid_request_exception.InvalidRequestException: <p>This exception occurs when there is something wrong with user input.</p>
        capo_emr.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_emr.types.start_session_input.StartSessionInput]",
    ) -> OperationResponse[
        "capo_emr.types.start_session_output.StartSessionOutput"
    ]:
        import capo_emr._operations.elastic_map_reduce.start_session

        output, http_response = (
            capo_emr._operations.elastic_map_reduce.start_session.start_session(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("StartSession")
    )
    input_: capo_emr.types.start_session_input.StartSessionInput = {}
    if name is not None:
        input_["name"] = name
    if cluster_id is not None:
        input_["cluster_id"] = cluster_id
    if execution_role_arn is not None:
        input_["execution_role_arn"] = execution_role_arn
    if engine_configurations is not None:
        input_["engine_configurations"] = engine_configurations
    if monitoring_configuration is not None:
        input_["monitoring_configuration"] = monitoring_configuration
    if session_idle_timeout_in_minutes is not None:
        input_["session_idle_timeout_in_minutes"] = session_idle_timeout_in_minutes
    if client_request_token is not None:
        input_["client_request_token"] = client_request_token
    if tags is not None:
        input_["tags"] = tags

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