create_application async ¶

async def create_application(
    self,
    release_label: "capo_emr_serverless.types.release_label.ReleaseLabel",
    type: "capo_emr_serverless.types.engine_type.EngineType",
    client_token: "capo_emr_serverless.types.client_token.ClientToken",
    *,
    config_overrides: Optional[AsyncEMRServerlessClientConfig] = None,
    name: Optional[
        "capo_emr_serverless.types.application_name.ApplicationName"
    ] = None,
    initial_capacity: Optional[
        "capo_emr_serverless.types.initial_capacity_config_map.InitialCapacityConfigMap"
    ] = None,
    maximum_capacity: Optional[
        "capo_emr_serverless.types.maximum_allowed_resources.MaximumAllowedResources"
    ] = None,
    tags: Optional["capo_emr_serverless.types.tag_map.TagMap"] = None,
    auto_start_configuration: Optional[
        "capo_emr_serverless.types.auto_start_config.AutoStartConfig"
    ] = None,
    auto_stop_configuration: Optional[
        "capo_emr_serverless.types.auto_stop_config.AutoStopConfig"
    ] = None,
    network_configuration: Optional[
        "capo_emr_serverless.types.network_configuration.NetworkConfiguration"
    ] = None,
    architecture: Optional[
        "capo_emr_serverless.types.architecture.Architecture"
    ] = None,
    image_configuration: Optional[
        "capo_emr_serverless.types.image_configuration_input.ImageConfigurationInput"
    ] = None,
    worker_type_specifications: Optional[
        "capo_emr_serverless.types.worker_type_specification_input_map.WorkerTypeSpecificationInputMap"
    ] = None,
    runtime_configuration: Optional[
        "capo_emr_serverless.types.configuration_list.ConfigurationList"
    ] = None,
    monitoring_configuration: Optional[
        "capo_emr_serverless.types.monitoring_configuration.MonitoringConfiguration"
    ] = None,
    disk_encryption_configuration: Optional[
        "capo_emr_serverless.types.disk_encryption_configuration.DiskEncryptionConfiguration"
    ] = None,
    interactive_configuration: Optional[
        "capo_emr_serverless.types.interactive_configuration.InteractiveConfiguration"
    ] = None,
    scheduler_configuration: Optional[
        "capo_emr_serverless.types.scheduler_configuration.SchedulerConfiguration"
    ] = None,
    identity_center_configuration: Optional[
        "capo_emr_serverless.types.identity_center_configuration_input.IdentityCenterConfigurationInput"
    ] = None,
    job_level_cost_allocation_configuration: Optional[
        "capo_emr_serverless.types.job_level_cost_allocation_configuration.JobLevelCostAllocationConfiguration"
    ] = None,
) -> "capo_emr_serverless.types.create_application_response.CreateApplicationResponse":
    """<p>Creates an application.</p>

    Args:
        name: <p>The name of the application.</p>
        release_label: <p>The Amazon EMR release associated with the application.</p>
        type: <p>The type of application you want to start, such as Spark or Hive.</p>
        client_token: <p>The client idempotency token of the application to create. Its value must be unique for each request.</p>
        initial_capacity: <p>The capacity to initialize when the application is created.</p>
        maximum_capacity: <p>The maximum capacity to allocate when the application is created. This is cumulative across all workers at any given point in time, not just when an application is created. No new resources will be created once any one of the defined limits is hit.</p>
        tags: <p>The tags assigned to the application.</p>
        auto_start_configuration: <p>The configuration for an application to automatically start on job submission.</p>
        auto_stop_configuration: <p>The configuration for an application to automatically stop after a certain amount of time being idle.</p>
        network_configuration: <p>The network configuration for customer VPC connectivity.</p>
        architecture: <p>The CPU architecture of an application.</p>
        image_configuration: <p>The image configuration for all worker types. You can either set this parameter or <code>imageConfiguration</code> for each worker type in <code>workerTypeSpecifications</code>.</p>
        worker_type_specifications: <p>The key-value pairs that specify worker type to <code>WorkerTypeSpecificationInput</code>. This parameter must contain all valid worker types for a Spark or Hive application. Valid worker types include <code>Driver</code> and <code>Executor</code> for Spark applications and <code>HiveDriver</code> and <code>TezTask</code> for Hive applications. You can either set image details in this parameter for each worker type, or in <code>imageConfiguration</code> for all worker types.</p>
        runtime_configuration: <p>The <a href="https://docs.aws.amazon.com/emr-serverless/latest/APIReference/API_Configuration.html">Configuration</a> specifications to use when creating an application. Each configuration consists of a classification and properties. This configuration is applied to all the job runs submitted under the application.</p>
        monitoring_configuration: <p>The configuration setting for monitoring.</p>
        disk_encryption_configuration: <p>The configuration object that allows encrypting local disks.</p>
        interactive_configuration: <p>The interactive configuration object that enables the interactive use cases to use when running an application.</p>
        scheduler_configuration: <p>The scheduler configuration for batch and streaming jobs running on this application. Supported with release labels emr-7.0.0 and above.</p>
        identity_center_configuration: <p>The IAM Identity Center Configuration accepts the Identity Center instance parameter required to enable trusted identity propagation. This configuration allows identity propagation between integrated services and the Identity Center instance.</p>
        job_level_cost_allocation_configuration: <p>The configuration object that enables job level cost allocation.</p>

    Raises:
        capo_emr_serverless.errors.conflict_exception.ConflictException: <p>The request could not be processed because of conflict in the current state of the resource.</p>
        capo_emr_serverless.errors.internal_server_exception.InternalServerException: <p>Request processing failed because of an error or failure with the service.</p>
        capo_emr_serverless.errors.resource_not_found_exception.ResourceNotFoundException: <p>The specified resource was not found.</p>
        capo_emr_serverless.errors.validation_exception.ValidationException: <p>The input fails to satisfy the constraints specified by an Amazon Web Services service.</p>
        capo_emr_serverless.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_emr_serverless.types.create_application_request.CreateApplicationRequest]",
    ) -> AsyncOperationResponse[
        "capo_emr_serverless.types.create_application_response.CreateApplicationResponse"
    ]:
        import capo_emr_serverless._operations.aws_toledo_web_service.create_application

        (
            output,
            http_response,
        ) = await capo_emr_serverless._operations.aws_toledo_web_service.create_application.async_create_application(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    def _redact_input(
        input_: "capo_emr_serverless.types.create_application_request.CreateApplicationRequest",
    ) -> dict[str, Any]:
        import capo_emr_serverless.types.create_application_request

        return (
            capo_emr_serverless.types.create_application_request.redact_sensitive(
                input_
            )
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("CreateApplication", redact_input=_redact_input),
    )
    input_: capo_emr_serverless.types.create_application_request.CreateApplicationRequest = {
        "release_label": release_label,
        "type": type,
        "client_token": client_token,
    }
    if name is not None:
        input_["name"] = name
    if initial_capacity is not None:
        input_["initial_capacity"] = initial_capacity
    if maximum_capacity is not None:
        input_["maximum_capacity"] = maximum_capacity
    if tags is not None:
        input_["tags"] = tags
    if auto_start_configuration is not None:
        input_["auto_start_configuration"] = auto_start_configuration
    if auto_stop_configuration is not None:
        input_["auto_stop_configuration"] = auto_stop_configuration
    if network_configuration is not None:
        input_["network_configuration"] = network_configuration
    if architecture is not None:
        input_["architecture"] = architecture
    if image_configuration is not None:
        input_["image_configuration"] = image_configuration
    if worker_type_specifications is not None:
        input_["worker_type_specifications"] = worker_type_specifications
    if runtime_configuration is not None:
        input_["runtime_configuration"] = runtime_configuration
    if monitoring_configuration is not None:
        input_["monitoring_configuration"] = monitoring_configuration
    if disk_encryption_configuration is not None:
        input_["disk_encryption_configuration"] = disk_encryption_configuration
    if interactive_configuration is not None:
        input_["interactive_configuration"] = interactive_configuration
    if scheduler_configuration is not None:
        input_["scheduler_configuration"] = scheduler_configuration
    if identity_center_configuration is not None:
        input_["identity_center_configuration"] = identity_center_configuration
    if job_level_cost_allocation_configuration is not None:
        input_["job_level_cost_allocation_configuration"] = (
            job_level_cost_allocation_configuration
        )

    response = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output