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Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
pulumi/pulumi-google-native

google-native.dataproc/v1.AutoscalingPolicy

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Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
pulumi/pulumi-google-native

    Creates new autoscaling policy. Auto-naming is currently not supported for this resource.

    Create AutoscalingPolicy Resource

    Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

    Constructor syntax

    new AutoscalingPolicy(name: string, args: AutoscalingPolicyArgs, opts?: CustomResourceOptions);
    @overload
def AutoscalingPolicy(resource_name: str,
                      args: AutoscalingPolicyArgs,
                      opts: Optional[ResourceOptions] = None)

@overload
def AutoscalingPolicy(resource_name: str,
                      opts: Optional[ResourceOptions] = None,
                      id: Optional[str] = None,
                      worker_config: Optional[InstanceGroupAutoscalingPolicyConfigArgs] = None,
                      basic_algorithm: Optional[BasicAutoscalingAlgorithmArgs] = None,
                      labels: Optional[Mapping[str, str]] = None,
                      location: Optional[str] = None,
                      project: Optional[str] = None,
                      secondary_worker_config: Optional[InstanceGroupAutoscalingPolicyConfigArgs] = None)
    func NewAutoscalingPolicy(ctx *Context, name string, args AutoscalingPolicyArgs, opts ...ResourceOption) (*AutoscalingPolicy, error)
    public AutoscalingPolicy(string name, AutoscalingPolicyArgs args, CustomResourceOptions? opts = null)
    public AutoscalingPolicy(String name, AutoscalingPolicyArgs args)
public AutoscalingPolicy(String name, AutoscalingPolicyArgs args, CustomResourceOptions options)

    type: google-native:dataproc/v1:AutoscalingPolicy
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

    Parameters

    name string
    The unique name of the resource.
    args AutoscalingPolicyArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    resource_name str
    The unique name of the resource.
    args AutoscalingPolicyArgs
    The arguments to resource properties.
    opts ResourceOptions
    Bag of options to control resource's behavior.
    ctx Context
    Context object for the current deployment.
    name string
    The unique name of the resource.
    args AutoscalingPolicyArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args AutoscalingPolicyArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args AutoscalingPolicyArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    Constructor example

    The following reference example uses placeholder values for all input properties.

    var autoscalingPolicyResource = new GoogleNative.Dataproc.V1.AutoscalingPolicy("autoscalingPolicyResource", new()
{
    Id = "string",
    WorkerConfig = new GoogleNative.Dataproc.V1.Inputs.InstanceGroupAutoscalingPolicyConfigArgs
    {
        MaxInstances = 0,
        MinInstances = 0,
        Weight = 0,
    },
    BasicAlgorithm = new GoogleNative.Dataproc.V1.Inputs.BasicAutoscalingAlgorithmArgs
    {
        CooldownPeriod = "string",
        SparkStandaloneConfig = new GoogleNative.Dataproc.V1.Inputs.SparkStandaloneAutoscalingConfigArgs
        {
            GracefulDecommissionTimeout = "string",
            ScaleDownFactor = 0,
            ScaleUpFactor = 0,
            RemoveOnlyIdleWorkers = false,
            ScaleDownMinWorkerFraction = 0,
            ScaleUpMinWorkerFraction = 0,
        },
        YarnConfig = new GoogleNative.Dataproc.V1.Inputs.BasicYarnAutoscalingConfigArgs
        {
            GracefulDecommissionTimeout = "string",
            ScaleDownFactor = 0,
            ScaleUpFactor = 0,
            ScaleDownMinWorkerFraction = 0,
            ScaleUpMinWorkerFraction = 0,
        },
    },
    Labels = 
    {
        { "string", "string" },
    },
    Location = "string",
    Project = "string",
    SecondaryWorkerConfig = new GoogleNative.Dataproc.V1.Inputs.InstanceGroupAutoscalingPolicyConfigArgs
    {
        MaxInstances = 0,
        MinInstances = 0,
        Weight = 0,
    },
});

    example, err := dataproc.NewAutoscalingPolicy(ctx, "autoscalingPolicyResource", &dataproc.AutoscalingPolicyArgs{
	Id: pulumi.String("string"),
	WorkerConfig: &dataproc.InstanceGroupAutoscalingPolicyConfigArgs{
		MaxInstances: pulumi.Int(0),
		MinInstances: pulumi.Int(0),
		Weight:       pulumi.Int(0),
	},
	BasicAlgorithm: &dataproc.BasicAutoscalingAlgorithmArgs{
		CooldownPeriod: pulumi.String("string"),
		SparkStandaloneConfig: &dataproc.SparkStandaloneAutoscalingConfigArgs{
			GracefulDecommissionTimeout: pulumi.String("string"),
			ScaleDownFactor:             pulumi.Float64(0),
			ScaleUpFactor:               pulumi.Float64(0),
			RemoveOnlyIdleWorkers:       pulumi.Bool(false),
			ScaleDownMinWorkerFraction:  pulumi.Float64(0),
			ScaleUpMinWorkerFraction:    pulumi.Float64(0),
		},
		YarnConfig: &dataproc.BasicYarnAutoscalingConfigArgs{
			GracefulDecommissionTimeout: pulumi.String("string"),
			ScaleDownFactor:             pulumi.Float64(0),
			ScaleUpFactor:               pulumi.Float64(0),
			ScaleDownMinWorkerFraction:  pulumi.Float64(0),
			ScaleUpMinWorkerFraction:    pulumi.Float64(0),
		},
	},
	Labels: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	Location: pulumi.String("string"),
	Project:  pulumi.String("string"),
	SecondaryWorkerConfig: &dataproc.InstanceGroupAutoscalingPolicyConfigArgs{
		MaxInstances: pulumi.Int(0),
		MinInstances: pulumi.Int(0),
		Weight:       pulumi.Int(0),
	},
})

    var autoscalingPolicyResource = new com.pulumi.googlenative.dataproc_v1.AutoscalingPolicy("autoscalingPolicyResource", com.pulumi.googlenative.dataproc_v1.AutoscalingPolicyArgs.builder()
    .id("string")
    .workerConfig(InstanceGroupAutoscalingPolicyConfigArgs.builder()
        .maxInstances(0)
        .minInstances(0)
        .weight(0)
        .build())
    .basicAlgorithm(BasicAutoscalingAlgorithmArgs.builder()
        .cooldownPeriod("string")
        .sparkStandaloneConfig(SparkStandaloneAutoscalingConfigArgs.builder()
            .gracefulDecommissionTimeout("string")
            .scaleDownFactor(0.0)
            .scaleUpFactor(0.0)
            .removeOnlyIdleWorkers(false)
            .scaleDownMinWorkerFraction(0.0)
            .scaleUpMinWorkerFraction(0.0)
            .build())
        .yarnConfig(BasicYarnAutoscalingConfigArgs.builder()
            .gracefulDecommissionTimeout("string")
            .scaleDownFactor(0.0)
            .scaleUpFactor(0.0)
            .scaleDownMinWorkerFraction(0.0)
            .scaleUpMinWorkerFraction(0.0)
            .build())
        .build())
    .labels(Map.of("string", "string"))
    .location("string")
    .project("string")
    .secondaryWorkerConfig(InstanceGroupAutoscalingPolicyConfigArgs.builder()
        .maxInstances(0)
        .minInstances(0)
        .weight(0)
        .build())
    .build());

    autoscaling_policy_resource = google_native.dataproc.v1.AutoscalingPolicy("autoscalingPolicyResource",
    id="string",
    worker_config={
        "max_instances": 0,
        "min_instances": 0,
        "weight": 0,
    },
    basic_algorithm={
        "cooldown_period": "string",
        "spark_standalone_config": {
            "graceful_decommission_timeout": "string",
            "scale_down_factor": 0,
            "scale_up_factor": 0,
            "remove_only_idle_workers": False,
            "scale_down_min_worker_fraction": 0,
            "scale_up_min_worker_fraction": 0,
        },
        "yarn_config": {
            "graceful_decommission_timeout": "string",
            "scale_down_factor": 0,
            "scale_up_factor": 0,
            "scale_down_min_worker_fraction": 0,
            "scale_up_min_worker_fraction": 0,
        },
    },
    labels={
        "string": "string",
    },
    location="string",
    project="string",
    secondary_worker_config={
        "max_instances": 0,
        "min_instances": 0,
        "weight": 0,
    })

    const autoscalingPolicyResource = new google_native.dataproc.v1.AutoscalingPolicy("autoscalingPolicyResource", {
    id: "string",
    workerConfig: {
        maxInstances: 0,
        minInstances: 0,
        weight: 0,
    },
    basicAlgorithm: {
        cooldownPeriod: "string",
        sparkStandaloneConfig: {
            gracefulDecommissionTimeout: "string",
            scaleDownFactor: 0,
            scaleUpFactor: 0,
            removeOnlyIdleWorkers: false,
            scaleDownMinWorkerFraction: 0,
            scaleUpMinWorkerFraction: 0,
        },
        yarnConfig: {
            gracefulDecommissionTimeout: "string",
            scaleDownFactor: 0,
            scaleUpFactor: 0,
            scaleDownMinWorkerFraction: 0,
            scaleUpMinWorkerFraction: 0,
        },
    },
    labels: {
        string: "string",
    },
    location: "string",
    project: "string",
    secondaryWorkerConfig: {
        maxInstances: 0,
        minInstances: 0,
        weight: 0,
    },
});

    type: google-native:dataproc/v1:AutoscalingPolicy
properties:
    basicAlgorithm:
        cooldownPeriod: string
        sparkStandaloneConfig:
            gracefulDecommissionTimeout: string
            removeOnlyIdleWorkers: false
            scaleDownFactor: 0
            scaleDownMinWorkerFraction: 0
            scaleUpFactor: 0
            scaleUpMinWorkerFraction: 0
        yarnConfig:
            gracefulDecommissionTimeout: string
            scaleDownFactor: 0
            scaleDownMinWorkerFraction: 0
            scaleUpFactor: 0
            scaleUpMinWorkerFraction: 0
    id: string
    labels:
        string: string
    location: string
    project: string
    secondaryWorkerConfig:
        maxInstances: 0
        minInstances: 0
        weight: 0
    workerConfig:
        maxInstances: 0
        minInstances: 0
        weight: 0

    AutoscalingPolicy Resource Properties

    To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

    Inputs

    In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.

    The AutoscalingPolicy resource accepts the following input properties:

    Id string
    The policy id.The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of between 3 and 50 characters.
    WorkerConfig Pulumi.GoogleNative.Dataproc.V1.Inputs.InstanceGroupAutoscalingPolicyConfig
    Describes how the autoscaler will operate for primary workers.
    BasicAlgorithm Pulumi.GoogleNative.Dataproc.V1.Inputs.BasicAutoscalingAlgorithm
    Labels Dictionary<string, string>
    Optional. The labels to associate with this autoscaling policy. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with an autoscaling policy.
    Location string
    Project string
    SecondaryWorkerConfig Pulumi.GoogleNative.Dataproc.V1.Inputs.InstanceGroupAutoscalingPolicyConfig
    Optional. Describes how the autoscaler will operate for secondary workers.
    Id string
    The policy id.The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of between 3 and 50 characters.
    WorkerConfig InstanceGroupAutoscalingPolicyConfigArgs
    Describes how the autoscaler will operate for primary workers.
    BasicAlgorithm BasicAutoscalingAlgorithmArgs
    Labels map[string]string
    Optional. The labels to associate with this autoscaling policy. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with an autoscaling policy.
    Location string
    Project string
    SecondaryWorkerConfig InstanceGroupAutoscalingPolicyConfigArgs
    Optional. Describes how the autoscaler will operate for secondary workers.
    id String
    The policy id.The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of between 3 and 50 characters.
    workerConfig InstanceGroupAutoscalingPolicyConfig
    Describes how the autoscaler will operate for primary workers.
    basicAlgorithm BasicAutoscalingAlgorithm
    labels Map<String,String>
    Optional. The labels to associate with this autoscaling policy. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with an autoscaling policy.
    location String
    project String
    secondaryWorkerConfig InstanceGroupAutoscalingPolicyConfig
    Optional. Describes how the autoscaler will operate for secondary workers.
    id string
    The policy id.The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of between 3 and 50 characters.
    workerConfig InstanceGroupAutoscalingPolicyConfig
    Describes how the autoscaler will operate for primary workers.
    basicAlgorithm BasicAutoscalingAlgorithm
    labels {[key: string]: string}
    Optional. The labels to associate with this autoscaling policy. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with an autoscaling policy.
    location string
    project string
    secondaryWorkerConfig InstanceGroupAutoscalingPolicyConfig
    Optional. Describes how the autoscaler will operate for secondary workers.
    id str
    The policy id.The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of between 3 and 50 characters.
    worker_config InstanceGroupAutoscalingPolicyConfigArgs
    Describes how the autoscaler will operate for primary workers.
    basic_algorithm BasicAutoscalingAlgorithmArgs
    labels Mapping[str, str]
    Optional. The labels to associate with this autoscaling policy. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with an autoscaling policy.
    location str
    project str
    secondary_worker_config InstanceGroupAutoscalingPolicyConfigArgs
    Optional. Describes how the autoscaler will operate for secondary workers.
    id String
    The policy id.The id must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of between 3 and 50 characters.
    workerConfig Property Map
    Describes how the autoscaler will operate for primary workers.
    basicAlgorithm Property Map
    labels Map<String>
    Optional. The labels to associate with this autoscaling policy. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with an autoscaling policy.
    location String
    project String
    secondaryWorkerConfig Property Map
    Optional. Describes how the autoscaler will operate for secondary workers.

    Outputs

    All input properties are implicitly available as output properties. Additionally, the AutoscalingPolicy resource produces the following output properties:

    Id string
    The provider-assigned unique ID for this managed resource.
    Name string
    The "resource name" of the autoscaling policy, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.autoscalingPolicies, the resource name of the policy has the following format: projects/{project_id}/regions/{region}/autoscalingPolicies/{policy_id} For projects.locations.autoscalingPolicies, the resource name of the policy has the following format: projects/{project_id}/locations/{location}/autoscalingPolicies/{policy_id}
    Id string
    The provider-assigned unique ID for this managed resource.
    Name string
    The "resource name" of the autoscaling policy, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.autoscalingPolicies, the resource name of the policy has the following format: projects/{project_id}/regions/{region}/autoscalingPolicies/{policy_id} For projects.locations.autoscalingPolicies, the resource name of the policy has the following format: projects/{project_id}/locations/{location}/autoscalingPolicies/{policy_id}
    id String
    The provider-assigned unique ID for this managed resource.
    name String
    The "resource name" of the autoscaling policy, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.autoscalingPolicies, the resource name of the policy has the following format: projects/{project_id}/regions/{region}/autoscalingPolicies/{policy_id} For projects.locations.autoscalingPolicies, the resource name of the policy has the following format: projects/{project_id}/locations/{location}/autoscalingPolicies/{policy_id}
    id string
    The provider-assigned unique ID for this managed resource.
    name string
    The "resource name" of the autoscaling policy, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.autoscalingPolicies, the resource name of the policy has the following format: projects/{project_id}/regions/{region}/autoscalingPolicies/{policy_id} For projects.locations.autoscalingPolicies, the resource name of the policy has the following format: projects/{project_id}/locations/{location}/autoscalingPolicies/{policy_id}
    id str
    The provider-assigned unique ID for this managed resource.
    name str
    The "resource name" of the autoscaling policy, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.autoscalingPolicies, the resource name of the policy has the following format: projects/{project_id}/regions/{region}/autoscalingPolicies/{policy_id} For projects.locations.autoscalingPolicies, the resource name of the policy has the following format: projects/{project_id}/locations/{location}/autoscalingPolicies/{policy_id}
    id String
    The provider-assigned unique ID for this managed resource.
    name String
    The "resource name" of the autoscaling policy, as described in https://cloud.google.com/apis/design/resource_names. For projects.regions.autoscalingPolicies, the resource name of the policy has the following format: projects/{project_id}/regions/{region}/autoscalingPolicies/{policy_id} For projects.locations.autoscalingPolicies, the resource name of the policy has the following format: projects/{project_id}/locations/{location}/autoscalingPolicies/{policy_id}

    Supporting Types

    BasicAutoscalingAlgorithm, BasicAutoscalingAlgorithmArgs

    CooldownPeriod string
    Optional. Duration between scaling events. A scaling period starts after the update operation from the previous event has completed.Bounds: 2m, 1d. Default: 2m.
    SparkStandaloneConfig Pulumi.GoogleNative.Dataproc.V1.Inputs.SparkStandaloneAutoscalingConfig
    Optional. Spark Standalone autoscaling configuration
    YarnConfig Pulumi.GoogleNative.Dataproc.V1.Inputs.BasicYarnAutoscalingConfig
    Optional. YARN autoscaling configuration.
    CooldownPeriod string
    Optional. Duration between scaling events. A scaling period starts after the update operation from the previous event has completed.Bounds: 2m, 1d. Default: 2m.
    SparkStandaloneConfig SparkStandaloneAutoscalingConfig
    Optional. Spark Standalone autoscaling configuration
    YarnConfig BasicYarnAutoscalingConfig
    Optional. YARN autoscaling configuration.
    cooldownPeriod String
    Optional. Duration between scaling events. A scaling period starts after the update operation from the previous event has completed.Bounds: 2m, 1d. Default: 2m.
    sparkStandaloneConfig SparkStandaloneAutoscalingConfig
    Optional. Spark Standalone autoscaling configuration
    yarnConfig BasicYarnAutoscalingConfig
    Optional. YARN autoscaling configuration.
    cooldownPeriod string
    Optional. Duration between scaling events. A scaling period starts after the update operation from the previous event has completed.Bounds: 2m, 1d. Default: 2m.
    sparkStandaloneConfig SparkStandaloneAutoscalingConfig
    Optional. Spark Standalone autoscaling configuration
    yarnConfig BasicYarnAutoscalingConfig
    Optional. YARN autoscaling configuration.
    cooldown_period str
    Optional. Duration between scaling events. A scaling period starts after the update operation from the previous event has completed.Bounds: 2m, 1d. Default: 2m.
    spark_standalone_config SparkStandaloneAutoscalingConfig
    Optional. Spark Standalone autoscaling configuration
    yarn_config BasicYarnAutoscalingConfig
    Optional. YARN autoscaling configuration.
    cooldownPeriod String
    Optional. Duration between scaling events. A scaling period starts after the update operation from the previous event has completed.Bounds: 2m, 1d. Default: 2m.
    sparkStandaloneConfig Property Map
    Optional. Spark Standalone autoscaling configuration
    yarnConfig Property Map
    Optional. YARN autoscaling configuration.

    BasicAutoscalingAlgorithmResponse, BasicAutoscalingAlgorithmResponseArgs

    CooldownPeriod string
    Optional. Duration between scaling events. A scaling period starts after the update operation from the previous event has completed.Bounds: 2m, 1d. Default: 2m.
    SparkStandaloneConfig Pulumi.GoogleNative.Dataproc.V1.Inputs.SparkStandaloneAutoscalingConfigResponse
    Optional. Spark Standalone autoscaling configuration
    YarnConfig Pulumi.GoogleNative.Dataproc.V1.Inputs.BasicYarnAutoscalingConfigResponse
    Optional. YARN autoscaling configuration.
    CooldownPeriod string
    Optional. Duration between scaling events. A scaling period starts after the update operation from the previous event has completed.Bounds: 2m, 1d. Default: 2m.
    SparkStandaloneConfig SparkStandaloneAutoscalingConfigResponse
    Optional. Spark Standalone autoscaling configuration
    YarnConfig BasicYarnAutoscalingConfigResponse
    Optional. YARN autoscaling configuration.
    cooldownPeriod String
    Optional. Duration between scaling events. A scaling period starts after the update operation from the previous event has completed.Bounds: 2m, 1d. Default: 2m.
    sparkStandaloneConfig SparkStandaloneAutoscalingConfigResponse
    Optional. Spark Standalone autoscaling configuration
    yarnConfig BasicYarnAutoscalingConfigResponse
    Optional. YARN autoscaling configuration.
    cooldownPeriod string
    Optional. Duration between scaling events. A scaling period starts after the update operation from the previous event has completed.Bounds: 2m, 1d. Default: 2m.
    sparkStandaloneConfig SparkStandaloneAutoscalingConfigResponse
    Optional. Spark Standalone autoscaling configuration
    yarnConfig BasicYarnAutoscalingConfigResponse
    Optional. YARN autoscaling configuration.
    cooldown_period str
    Optional. Duration between scaling events. A scaling period starts after the update operation from the previous event has completed.Bounds: 2m, 1d. Default: 2m.
    spark_standalone_config SparkStandaloneAutoscalingConfigResponse
    Optional. Spark Standalone autoscaling configuration
    yarn_config BasicYarnAutoscalingConfigResponse
    Optional. YARN autoscaling configuration.
    cooldownPeriod String
    Optional. Duration between scaling events. A scaling period starts after the update operation from the previous event has completed.Bounds: 2m, 1d. Default: 2m.
    sparkStandaloneConfig Property Map
    Optional. Spark Standalone autoscaling configuration
    yarnConfig Property Map
    Optional. YARN autoscaling configuration.

    BasicYarnAutoscalingConfig, BasicYarnAutoscalingConfigArgs

    GracefulDecommissionTimeout string
    Timeout for YARN graceful decommissioning of Node Managers. Specifies the duration to wait for jobs to complete before forcefully removing workers (and potentially interrupting jobs). Only applicable to downscaling operations.Bounds: 0s, 1d.
    ScaleDownFactor double
    Fraction of average YARN pending memory in the last cooldown period for which to remove workers. A scale-down factor of 1 will result in scaling down so that there is no available memory remaining after the update (more aggressive scaling). A scale-down factor of 0 disables removing workers, which can be beneficial for autoscaling a single job. See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    ScaleUpFactor double
    Fraction of average YARN pending memory in the last cooldown period for which to add workers. A scale-up factor of 1.0 will result in scaling up so that there is no pending memory remaining after the update (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling). See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    ScaleDownMinWorkerFraction double
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    ScaleUpMinWorkerFraction double
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    GracefulDecommissionTimeout string
    Timeout for YARN graceful decommissioning of Node Managers. Specifies the duration to wait for jobs to complete before forcefully removing workers (and potentially interrupting jobs). Only applicable to downscaling operations.Bounds: 0s, 1d.
    ScaleDownFactor float64
    Fraction of average YARN pending memory in the last cooldown period for which to remove workers. A scale-down factor of 1 will result in scaling down so that there is no available memory remaining after the update (more aggressive scaling). A scale-down factor of 0 disables removing workers, which can be beneficial for autoscaling a single job. See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    ScaleUpFactor float64
    Fraction of average YARN pending memory in the last cooldown period for which to add workers. A scale-up factor of 1.0 will result in scaling up so that there is no pending memory remaining after the update (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling). See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    ScaleDownMinWorkerFraction float64
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    ScaleUpMinWorkerFraction float64
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    gracefulDecommissionTimeout String
    Timeout for YARN graceful decommissioning of Node Managers. Specifies the duration to wait for jobs to complete before forcefully removing workers (and potentially interrupting jobs). Only applicable to downscaling operations.Bounds: 0s, 1d.
    scaleDownFactor Double
    Fraction of average YARN pending memory in the last cooldown period for which to remove workers. A scale-down factor of 1 will result in scaling down so that there is no available memory remaining after the update (more aggressive scaling). A scale-down factor of 0 disables removing workers, which can be beneficial for autoscaling a single job. See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    scaleUpFactor Double
    Fraction of average YARN pending memory in the last cooldown period for which to add workers. A scale-up factor of 1.0 will result in scaling up so that there is no pending memory remaining after the update (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling). See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    scaleDownMinWorkerFraction Double
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    scaleUpMinWorkerFraction Double
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    gracefulDecommissionTimeout string
    Timeout for YARN graceful decommissioning of Node Managers. Specifies the duration to wait for jobs to complete before forcefully removing workers (and potentially interrupting jobs). Only applicable to downscaling operations.Bounds: 0s, 1d.
    scaleDownFactor number
    Fraction of average YARN pending memory in the last cooldown period for which to remove workers. A scale-down factor of 1 will result in scaling down so that there is no available memory remaining after the update (more aggressive scaling). A scale-down factor of 0 disables removing workers, which can be beneficial for autoscaling a single job. See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    scaleUpFactor number
    Fraction of average YARN pending memory in the last cooldown period for which to add workers. A scale-up factor of 1.0 will result in scaling up so that there is no pending memory remaining after the update (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling). See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    scaleDownMinWorkerFraction number
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    scaleUpMinWorkerFraction number
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    graceful_decommission_timeout str
    Timeout for YARN graceful decommissioning of Node Managers. Specifies the duration to wait for jobs to complete before forcefully removing workers (and potentially interrupting jobs). Only applicable to downscaling operations.Bounds: 0s, 1d.
    scale_down_factor float
    Fraction of average YARN pending memory in the last cooldown period for which to remove workers. A scale-down factor of 1 will result in scaling down so that there is no available memory remaining after the update (more aggressive scaling). A scale-down factor of 0 disables removing workers, which can be beneficial for autoscaling a single job. See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    scale_up_factor float
    Fraction of average YARN pending memory in the last cooldown period for which to add workers. A scale-up factor of 1.0 will result in scaling up so that there is no pending memory remaining after the update (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling). See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    scale_down_min_worker_fraction float
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    scale_up_min_worker_fraction float
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    gracefulDecommissionTimeout String
    Timeout for YARN graceful decommissioning of Node Managers. Specifies the duration to wait for jobs to complete before forcefully removing workers (and potentially interrupting jobs). Only applicable to downscaling operations.Bounds: 0s, 1d.
    scaleDownFactor Number
    Fraction of average YARN pending memory in the last cooldown period for which to remove workers. A scale-down factor of 1 will result in scaling down so that there is no available memory remaining after the update (more aggressive scaling). A scale-down factor of 0 disables removing workers, which can be beneficial for autoscaling a single job. See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    scaleUpFactor Number
    Fraction of average YARN pending memory in the last cooldown period for which to add workers. A scale-up factor of 1.0 will result in scaling up so that there is no pending memory remaining after the update (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling). See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    scaleDownMinWorkerFraction Number
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    scaleUpMinWorkerFraction Number
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.

    BasicYarnAutoscalingConfigResponse, BasicYarnAutoscalingConfigResponseArgs

    GracefulDecommissionTimeout string
    Timeout for YARN graceful decommissioning of Node Managers. Specifies the duration to wait for jobs to complete before forcefully removing workers (and potentially interrupting jobs). Only applicable to downscaling operations.Bounds: 0s, 1d.
    ScaleDownFactor double
    Fraction of average YARN pending memory in the last cooldown period for which to remove workers. A scale-down factor of 1 will result in scaling down so that there is no available memory remaining after the update (more aggressive scaling). A scale-down factor of 0 disables removing workers, which can be beneficial for autoscaling a single job. See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    ScaleDownMinWorkerFraction double
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    ScaleUpFactor double
    Fraction of average YARN pending memory in the last cooldown period for which to add workers. A scale-up factor of 1.0 will result in scaling up so that there is no pending memory remaining after the update (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling). See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    ScaleUpMinWorkerFraction double
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    GracefulDecommissionTimeout string
    Timeout for YARN graceful decommissioning of Node Managers. Specifies the duration to wait for jobs to complete before forcefully removing workers (and potentially interrupting jobs). Only applicable to downscaling operations.Bounds: 0s, 1d.
    ScaleDownFactor float64
    Fraction of average YARN pending memory in the last cooldown period for which to remove workers. A scale-down factor of 1 will result in scaling down so that there is no available memory remaining after the update (more aggressive scaling). A scale-down factor of 0 disables removing workers, which can be beneficial for autoscaling a single job. See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    ScaleDownMinWorkerFraction float64
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    ScaleUpFactor float64
    Fraction of average YARN pending memory in the last cooldown period for which to add workers. A scale-up factor of 1.0 will result in scaling up so that there is no pending memory remaining after the update (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling). See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    ScaleUpMinWorkerFraction float64
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    gracefulDecommissionTimeout String
    Timeout for YARN graceful decommissioning of Node Managers. Specifies the duration to wait for jobs to complete before forcefully removing workers (and potentially interrupting jobs). Only applicable to downscaling operations.Bounds: 0s, 1d.
    scaleDownFactor Double
    Fraction of average YARN pending memory in the last cooldown period for which to remove workers. A scale-down factor of 1 will result in scaling down so that there is no available memory remaining after the update (more aggressive scaling). A scale-down factor of 0 disables removing workers, which can be beneficial for autoscaling a single job. See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    scaleDownMinWorkerFraction Double
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    scaleUpFactor Double
    Fraction of average YARN pending memory in the last cooldown period for which to add workers. A scale-up factor of 1.0 will result in scaling up so that there is no pending memory remaining after the update (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling). See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    scaleUpMinWorkerFraction Double
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    gracefulDecommissionTimeout string
    Timeout for YARN graceful decommissioning of Node Managers. Specifies the duration to wait for jobs to complete before forcefully removing workers (and potentially interrupting jobs). Only applicable to downscaling operations.Bounds: 0s, 1d.
    scaleDownFactor number
    Fraction of average YARN pending memory in the last cooldown period for which to remove workers. A scale-down factor of 1 will result in scaling down so that there is no available memory remaining after the update (more aggressive scaling). A scale-down factor of 0 disables removing workers, which can be beneficial for autoscaling a single job. See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    scaleDownMinWorkerFraction number
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    scaleUpFactor number
    Fraction of average YARN pending memory in the last cooldown period for which to add workers. A scale-up factor of 1.0 will result in scaling up so that there is no pending memory remaining after the update (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling). See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    scaleUpMinWorkerFraction number
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    graceful_decommission_timeout str
    Timeout for YARN graceful decommissioning of Node Managers. Specifies the duration to wait for jobs to complete before forcefully removing workers (and potentially interrupting jobs). Only applicable to downscaling operations.Bounds: 0s, 1d.
    scale_down_factor float
    Fraction of average YARN pending memory in the last cooldown period for which to remove workers. A scale-down factor of 1 will result in scaling down so that there is no available memory remaining after the update (more aggressive scaling). A scale-down factor of 0 disables removing workers, which can be beneficial for autoscaling a single job. See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    scale_down_min_worker_fraction float
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    scale_up_factor float
    Fraction of average YARN pending memory in the last cooldown period for which to add workers. A scale-up factor of 1.0 will result in scaling up so that there is no pending memory remaining after the update (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling). See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    scale_up_min_worker_fraction float
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    gracefulDecommissionTimeout String
    Timeout for YARN graceful decommissioning of Node Managers. Specifies the duration to wait for jobs to complete before forcefully removing workers (and potentially interrupting jobs). Only applicable to downscaling operations.Bounds: 0s, 1d.
    scaleDownFactor Number
    Fraction of average YARN pending memory in the last cooldown period for which to remove workers. A scale-down factor of 1 will result in scaling down so that there is no available memory remaining after the update (more aggressive scaling). A scale-down factor of 0 disables removing workers, which can be beneficial for autoscaling a single job. See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    scaleDownMinWorkerFraction Number
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    scaleUpFactor Number
    Fraction of average YARN pending memory in the last cooldown period for which to add workers. A scale-up factor of 1.0 will result in scaling up so that there is no pending memory remaining after the update (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling). See How autoscaling works (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/autoscaling#how_autoscaling_works) for more information.Bounds: 0.0, 1.0.
    scaleUpMinWorkerFraction Number
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.

    InstanceGroupAutoscalingPolicyConfig, InstanceGroupAutoscalingPolicyConfigArgs

    MaxInstances int
    Maximum number of instances for this group. Required for primary workers. Note that by default, clusters will not use secondary workers. Required for secondary workers if the minimum secondary instances is set.Primary workers - Bounds: [min_instances, ). Secondary workers - Bounds: [min_instances, ). Default: 0.
    MinInstances int
    Optional. Minimum number of instances for this group.Primary workers - Bounds: 2, max_instances. Default: 2. Secondary workers - Bounds: 0, max_instances. Default: 0.
    Weight int
    Optional. Weight for the instance group, which is used to determine the fraction of total workers in the cluster from this instance group. For example, if primary workers have weight 2, and secondary workers have weight 1, the cluster will have approximately 2 primary workers for each secondary worker.The cluster may not reach the specified balance if constrained by min/max bounds or other autoscaling settings. For example, if max_instances for secondary workers is 0, then only primary workers will be added. The cluster can also be out of balance when created.If weight is not set on any instance group, the cluster will default to equal weight for all groups: the cluster will attempt to maintain an equal number of workers in each group within the configured size bounds for each group. If weight is set for one group only, the cluster will default to zero weight on the unset group. For example if weight is set only on primary workers, the cluster will use primary workers only and no secondary workers.
    MaxInstances int
    Maximum number of instances for this group. Required for primary workers. Note that by default, clusters will not use secondary workers. Required for secondary workers if the minimum secondary instances is set.Primary workers - Bounds: [min_instances, ). Secondary workers - Bounds: [min_instances, ). Default: 0.
    MinInstances int
    Optional. Minimum number of instances for this group.Primary workers - Bounds: 2, max_instances. Default: 2. Secondary workers - Bounds: 0, max_instances. Default: 0.
    Weight int
    Optional. Weight for the instance group, which is used to determine the fraction of total workers in the cluster from this instance group. For example, if primary workers have weight 2, and secondary workers have weight 1, the cluster will have approximately 2 primary workers for each secondary worker.The cluster may not reach the specified balance if constrained by min/max bounds or other autoscaling settings. For example, if max_instances for secondary workers is 0, then only primary workers will be added. The cluster can also be out of balance when created.If weight is not set on any instance group, the cluster will default to equal weight for all groups: the cluster will attempt to maintain an equal number of workers in each group within the configured size bounds for each group. If weight is set for one group only, the cluster will default to zero weight on the unset group. For example if weight is set only on primary workers, the cluster will use primary workers only and no secondary workers.
    maxInstances Integer
    Maximum number of instances for this group. Required for primary workers. Note that by default, clusters will not use secondary workers. Required for secondary workers if the minimum secondary instances is set.Primary workers - Bounds: [min_instances, ). Secondary workers - Bounds: [min_instances, ). Default: 0.
    minInstances Integer
    Optional. Minimum number of instances for this group.Primary workers - Bounds: 2, max_instances. Default: 2. Secondary workers - Bounds: 0, max_instances. Default: 0.
    weight Integer
    Optional. Weight for the instance group, which is used to determine the fraction of total workers in the cluster from this instance group. For example, if primary workers have weight 2, and secondary workers have weight 1, the cluster will have approximately 2 primary workers for each secondary worker.The cluster may not reach the specified balance if constrained by min/max bounds or other autoscaling settings. For example, if max_instances for secondary workers is 0, then only primary workers will be added. The cluster can also be out of balance when created.If weight is not set on any instance group, the cluster will default to equal weight for all groups: the cluster will attempt to maintain an equal number of workers in each group within the configured size bounds for each group. If weight is set for one group only, the cluster will default to zero weight on the unset group. For example if weight is set only on primary workers, the cluster will use primary workers only and no secondary workers.
    maxInstances number
    Maximum number of instances for this group. Required for primary workers. Note that by default, clusters will not use secondary workers. Required for secondary workers if the minimum secondary instances is set.Primary workers - Bounds: [min_instances, ). Secondary workers - Bounds: [min_instances, ). Default: 0.
    minInstances number
    Optional. Minimum number of instances for this group.Primary workers - Bounds: 2, max_instances. Default: 2. Secondary workers - Bounds: 0, max_instances. Default: 0.
    weight number
    Optional. Weight for the instance group, which is used to determine the fraction of total workers in the cluster from this instance group. For example, if primary workers have weight 2, and secondary workers have weight 1, the cluster will have approximately 2 primary workers for each secondary worker.The cluster may not reach the specified balance if constrained by min/max bounds or other autoscaling settings. For example, if max_instances for secondary workers is 0, then only primary workers will be added. The cluster can also be out of balance when created.If weight is not set on any instance group, the cluster will default to equal weight for all groups: the cluster will attempt to maintain an equal number of workers in each group within the configured size bounds for each group. If weight is set for one group only, the cluster will default to zero weight on the unset group. For example if weight is set only on primary workers, the cluster will use primary workers only and no secondary workers.
    max_instances int
    Maximum number of instances for this group. Required for primary workers. Note that by default, clusters will not use secondary workers. Required for secondary workers if the minimum secondary instances is set.Primary workers - Bounds: [min_instances, ). Secondary workers - Bounds: [min_instances, ). Default: 0.
    min_instances int
    Optional. Minimum number of instances for this group.Primary workers - Bounds: 2, max_instances. Default: 2. Secondary workers - Bounds: 0, max_instances. Default: 0.
    weight int
    Optional. Weight for the instance group, which is used to determine the fraction of total workers in the cluster from this instance group. For example, if primary workers have weight 2, and secondary workers have weight 1, the cluster will have approximately 2 primary workers for each secondary worker.The cluster may not reach the specified balance if constrained by min/max bounds or other autoscaling settings. For example, if max_instances for secondary workers is 0, then only primary workers will be added. The cluster can also be out of balance when created.If weight is not set on any instance group, the cluster will default to equal weight for all groups: the cluster will attempt to maintain an equal number of workers in each group within the configured size bounds for each group. If weight is set for one group only, the cluster will default to zero weight on the unset group. For example if weight is set only on primary workers, the cluster will use primary workers only and no secondary workers.
    maxInstances Number
    Maximum number of instances for this group. Required for primary workers. Note that by default, clusters will not use secondary workers. Required for secondary workers if the minimum secondary instances is set.Primary workers - Bounds: [min_instances, ). Secondary workers - Bounds: [min_instances, ). Default: 0.
    minInstances Number
    Optional. Minimum number of instances for this group.Primary workers - Bounds: 2, max_instances. Default: 2. Secondary workers - Bounds: 0, max_instances. Default: 0.
    weight Number
    Optional. Weight for the instance group, which is used to determine the fraction of total workers in the cluster from this instance group. For example, if primary workers have weight 2, and secondary workers have weight 1, the cluster will have approximately 2 primary workers for each secondary worker.The cluster may not reach the specified balance if constrained by min/max bounds or other autoscaling settings. For example, if max_instances for secondary workers is 0, then only primary workers will be added. The cluster can also be out of balance when created.If weight is not set on any instance group, the cluster will default to equal weight for all groups: the cluster will attempt to maintain an equal number of workers in each group within the configured size bounds for each group. If weight is set for one group only, the cluster will default to zero weight on the unset group. For example if weight is set only on primary workers, the cluster will use primary workers only and no secondary workers.

    InstanceGroupAutoscalingPolicyConfigResponse, InstanceGroupAutoscalingPolicyConfigResponseArgs

    MaxInstances int
    Maximum number of instances for this group. Required for primary workers. Note that by default, clusters will not use secondary workers. Required for secondary workers if the minimum secondary instances is set.Primary workers - Bounds: [min_instances, ). Secondary workers - Bounds: [min_instances, ). Default: 0.
    MinInstances int
    Optional. Minimum number of instances for this group.Primary workers - Bounds: 2, max_instances. Default: 2. Secondary workers - Bounds: 0, max_instances. Default: 0.
    Weight int
    Optional. Weight for the instance group, which is used to determine the fraction of total workers in the cluster from this instance group. For example, if primary workers have weight 2, and secondary workers have weight 1, the cluster will have approximately 2 primary workers for each secondary worker.The cluster may not reach the specified balance if constrained by min/max bounds or other autoscaling settings. For example, if max_instances for secondary workers is 0, then only primary workers will be added. The cluster can also be out of balance when created.If weight is not set on any instance group, the cluster will default to equal weight for all groups: the cluster will attempt to maintain an equal number of workers in each group within the configured size bounds for each group. If weight is set for one group only, the cluster will default to zero weight on the unset group. For example if weight is set only on primary workers, the cluster will use primary workers only and no secondary workers.
    MaxInstances int
    Maximum number of instances for this group. Required for primary workers. Note that by default, clusters will not use secondary workers. Required for secondary workers if the minimum secondary instances is set.Primary workers - Bounds: [min_instances, ). Secondary workers - Bounds: [min_instances, ). Default: 0.
    MinInstances int
    Optional. Minimum number of instances for this group.Primary workers - Bounds: 2, max_instances. Default: 2. Secondary workers - Bounds: 0, max_instances. Default: 0.
    Weight int
    Optional. Weight for the instance group, which is used to determine the fraction of total workers in the cluster from this instance group. For example, if primary workers have weight 2, and secondary workers have weight 1, the cluster will have approximately 2 primary workers for each secondary worker.The cluster may not reach the specified balance if constrained by min/max bounds or other autoscaling settings. For example, if max_instances for secondary workers is 0, then only primary workers will be added. The cluster can also be out of balance when created.If weight is not set on any instance group, the cluster will default to equal weight for all groups: the cluster will attempt to maintain an equal number of workers in each group within the configured size bounds for each group. If weight is set for one group only, the cluster will default to zero weight on the unset group. For example if weight is set only on primary workers, the cluster will use primary workers only and no secondary workers.
    maxInstances Integer
    Maximum number of instances for this group. Required for primary workers. Note that by default, clusters will not use secondary workers. Required for secondary workers if the minimum secondary instances is set.Primary workers - Bounds: [min_instances, ). Secondary workers - Bounds: [min_instances, ). Default: 0.
    minInstances Integer
    Optional. Minimum number of instances for this group.Primary workers - Bounds: 2, max_instances. Default: 2. Secondary workers - Bounds: 0, max_instances. Default: 0.
    weight Integer
    Optional. Weight for the instance group, which is used to determine the fraction of total workers in the cluster from this instance group. For example, if primary workers have weight 2, and secondary workers have weight 1, the cluster will have approximately 2 primary workers for each secondary worker.The cluster may not reach the specified balance if constrained by min/max bounds or other autoscaling settings. For example, if max_instances for secondary workers is 0, then only primary workers will be added. The cluster can also be out of balance when created.If weight is not set on any instance group, the cluster will default to equal weight for all groups: the cluster will attempt to maintain an equal number of workers in each group within the configured size bounds for each group. If weight is set for one group only, the cluster will default to zero weight on the unset group. For example if weight is set only on primary workers, the cluster will use primary workers only and no secondary workers.
    maxInstances number
    Maximum number of instances for this group. Required for primary workers. Note that by default, clusters will not use secondary workers. Required for secondary workers if the minimum secondary instances is set.Primary workers - Bounds: [min_instances, ). Secondary workers - Bounds: [min_instances, ). Default: 0.
    minInstances number
    Optional. Minimum number of instances for this group.Primary workers - Bounds: 2, max_instances. Default: 2. Secondary workers - Bounds: 0, max_instances. Default: 0.
    weight number
    Optional. Weight for the instance group, which is used to determine the fraction of total workers in the cluster from this instance group. For example, if primary workers have weight 2, and secondary workers have weight 1, the cluster will have approximately 2 primary workers for each secondary worker.The cluster may not reach the specified balance if constrained by min/max bounds or other autoscaling settings. For example, if max_instances for secondary workers is 0, then only primary workers will be added. The cluster can also be out of balance when created.If weight is not set on any instance group, the cluster will default to equal weight for all groups: the cluster will attempt to maintain an equal number of workers in each group within the configured size bounds for each group. If weight is set for one group only, the cluster will default to zero weight on the unset group. For example if weight is set only on primary workers, the cluster will use primary workers only and no secondary workers.
    max_instances int
    Maximum number of instances for this group. Required for primary workers. Note that by default, clusters will not use secondary workers. Required for secondary workers if the minimum secondary instances is set.Primary workers - Bounds: [min_instances, ). Secondary workers - Bounds: [min_instances, ). Default: 0.
    min_instances int
    Optional. Minimum number of instances for this group.Primary workers - Bounds: 2, max_instances. Default: 2. Secondary workers - Bounds: 0, max_instances. Default: 0.
    weight int
    Optional. Weight for the instance group, which is used to determine the fraction of total workers in the cluster from this instance group. For example, if primary workers have weight 2, and secondary workers have weight 1, the cluster will have approximately 2 primary workers for each secondary worker.The cluster may not reach the specified balance if constrained by min/max bounds or other autoscaling settings. For example, if max_instances for secondary workers is 0, then only primary workers will be added. The cluster can also be out of balance when created.If weight is not set on any instance group, the cluster will default to equal weight for all groups: the cluster will attempt to maintain an equal number of workers in each group within the configured size bounds for each group. If weight is set for one group only, the cluster will default to zero weight on the unset group. For example if weight is set only on primary workers, the cluster will use primary workers only and no secondary workers.
    maxInstances Number
    Maximum number of instances for this group. Required for primary workers. Note that by default, clusters will not use secondary workers. Required for secondary workers if the minimum secondary instances is set.Primary workers - Bounds: [min_instances, ). Secondary workers - Bounds: [min_instances, ). Default: 0.
    minInstances Number
    Optional. Minimum number of instances for this group.Primary workers - Bounds: 2, max_instances. Default: 2. Secondary workers - Bounds: 0, max_instances. Default: 0.
    weight Number
    Optional. Weight for the instance group, which is used to determine the fraction of total workers in the cluster from this instance group. For example, if primary workers have weight 2, and secondary workers have weight 1, the cluster will have approximately 2 primary workers for each secondary worker.The cluster may not reach the specified balance if constrained by min/max bounds or other autoscaling settings. For example, if max_instances for secondary workers is 0, then only primary workers will be added. The cluster can also be out of balance when created.If weight is not set on any instance group, the cluster will default to equal weight for all groups: the cluster will attempt to maintain an equal number of workers in each group within the configured size bounds for each group. If weight is set for one group only, the cluster will default to zero weight on the unset group. For example if weight is set only on primary workers, the cluster will use primary workers only and no secondary workers.

    SparkStandaloneAutoscalingConfig, SparkStandaloneAutoscalingConfigArgs

    GracefulDecommissionTimeout string
    Timeout for Spark graceful decommissioning of spark workers. Specifies the duration to wait for spark worker to complete spark decommissioning tasks before forcefully removing workers. Only applicable to downscaling operations.Bounds: 0s, 1d.
    ScaleDownFactor double
    Fraction of required executors to remove from Spark Serverless clusters. A scale-down factor of 1.0 will result in scaling down so that there are no more executors for the Spark Job.(more aggressive scaling). A scale-down factor closer to 0 will result in a smaller magnitude of scaling donw (less aggressive scaling).Bounds: 0.0, 1.0.
    ScaleUpFactor double
    Fraction of required workers to add to Spark Standalone clusters. A scale-up factor of 1.0 will result in scaling up so that there are no more required workers for the Spark Job (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling).Bounds: 0.0, 1.0.
    RemoveOnlyIdleWorkers bool
    Optional. Remove only idle workers when scaling down cluster
    ScaleDownMinWorkerFraction double
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    ScaleUpMinWorkerFraction double
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    GracefulDecommissionTimeout string
    Timeout for Spark graceful decommissioning of spark workers. Specifies the duration to wait for spark worker to complete spark decommissioning tasks before forcefully removing workers. Only applicable to downscaling operations.Bounds: 0s, 1d.
    ScaleDownFactor float64
    Fraction of required executors to remove from Spark Serverless clusters. A scale-down factor of 1.0 will result in scaling down so that there are no more executors for the Spark Job.(more aggressive scaling). A scale-down factor closer to 0 will result in a smaller magnitude of scaling donw (less aggressive scaling).Bounds: 0.0, 1.0.
    ScaleUpFactor float64
    Fraction of required workers to add to Spark Standalone clusters. A scale-up factor of 1.0 will result in scaling up so that there are no more required workers for the Spark Job (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling).Bounds: 0.0, 1.0.
    RemoveOnlyIdleWorkers bool
    Optional. Remove only idle workers when scaling down cluster
    ScaleDownMinWorkerFraction float64
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    ScaleUpMinWorkerFraction float64
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    gracefulDecommissionTimeout String
    Timeout for Spark graceful decommissioning of spark workers. Specifies the duration to wait for spark worker to complete spark decommissioning tasks before forcefully removing workers. Only applicable to downscaling operations.Bounds: 0s, 1d.
    scaleDownFactor Double
    Fraction of required executors to remove from Spark Serverless clusters. A scale-down factor of 1.0 will result in scaling down so that there are no more executors for the Spark Job.(more aggressive scaling). A scale-down factor closer to 0 will result in a smaller magnitude of scaling donw (less aggressive scaling).Bounds: 0.0, 1.0.
    scaleUpFactor Double
    Fraction of required workers to add to Spark Standalone clusters. A scale-up factor of 1.0 will result in scaling up so that there are no more required workers for the Spark Job (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling).Bounds: 0.0, 1.0.
    removeOnlyIdleWorkers Boolean
    Optional. Remove only idle workers when scaling down cluster
    scaleDownMinWorkerFraction Double
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    scaleUpMinWorkerFraction Double
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    gracefulDecommissionTimeout string
    Timeout for Spark graceful decommissioning of spark workers. Specifies the duration to wait for spark worker to complete spark decommissioning tasks before forcefully removing workers. Only applicable to downscaling operations.Bounds: 0s, 1d.
    scaleDownFactor number
    Fraction of required executors to remove from Spark Serverless clusters. A scale-down factor of 1.0 will result in scaling down so that there are no more executors for the Spark Job.(more aggressive scaling). A scale-down factor closer to 0 will result in a smaller magnitude of scaling donw (less aggressive scaling).Bounds: 0.0, 1.0.
    scaleUpFactor number
    Fraction of required workers to add to Spark Standalone clusters. A scale-up factor of 1.0 will result in scaling up so that there are no more required workers for the Spark Job (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling).Bounds: 0.0, 1.0.
    removeOnlyIdleWorkers boolean
    Optional. Remove only idle workers when scaling down cluster
    scaleDownMinWorkerFraction number
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    scaleUpMinWorkerFraction number
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    graceful_decommission_timeout str
    Timeout for Spark graceful decommissioning of spark workers. Specifies the duration to wait for spark worker to complete spark decommissioning tasks before forcefully removing workers. Only applicable to downscaling operations.Bounds: 0s, 1d.
    scale_down_factor float
    Fraction of required executors to remove from Spark Serverless clusters. A scale-down factor of 1.0 will result in scaling down so that there are no more executors for the Spark Job.(more aggressive scaling). A scale-down factor closer to 0 will result in a smaller magnitude of scaling donw (less aggressive scaling).Bounds: 0.0, 1.0.
    scale_up_factor float
    Fraction of required workers to add to Spark Standalone clusters. A scale-up factor of 1.0 will result in scaling up so that there are no more required workers for the Spark Job (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling).Bounds: 0.0, 1.0.
    remove_only_idle_workers bool
    Optional. Remove only idle workers when scaling down cluster
    scale_down_min_worker_fraction float
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    scale_up_min_worker_fraction float
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    gracefulDecommissionTimeout String
    Timeout for Spark graceful decommissioning of spark workers. Specifies the duration to wait for spark worker to complete spark decommissioning tasks before forcefully removing workers. Only applicable to downscaling operations.Bounds: 0s, 1d.
    scaleDownFactor Number
    Fraction of required executors to remove from Spark Serverless clusters. A scale-down factor of 1.0 will result in scaling down so that there are no more executors for the Spark Job.(more aggressive scaling). A scale-down factor closer to 0 will result in a smaller magnitude of scaling donw (less aggressive scaling).Bounds: 0.0, 1.0.
    scaleUpFactor Number
    Fraction of required workers to add to Spark Standalone clusters. A scale-up factor of 1.0 will result in scaling up so that there are no more required workers for the Spark Job (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling).Bounds: 0.0, 1.0.
    removeOnlyIdleWorkers Boolean
    Optional. Remove only idle workers when scaling down cluster
    scaleDownMinWorkerFraction Number
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    scaleUpMinWorkerFraction Number
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.

    SparkStandaloneAutoscalingConfigResponse, SparkStandaloneAutoscalingConfigResponseArgs

    GracefulDecommissionTimeout string
    Timeout for Spark graceful decommissioning of spark workers. Specifies the duration to wait for spark worker to complete spark decommissioning tasks before forcefully removing workers. Only applicable to downscaling operations.Bounds: 0s, 1d.
    RemoveOnlyIdleWorkers bool
    Optional. Remove only idle workers when scaling down cluster
    ScaleDownFactor double
    Fraction of required executors to remove from Spark Serverless clusters. A scale-down factor of 1.0 will result in scaling down so that there are no more executors for the Spark Job.(more aggressive scaling). A scale-down factor closer to 0 will result in a smaller magnitude of scaling donw (less aggressive scaling).Bounds: 0.0, 1.0.
    ScaleDownMinWorkerFraction double
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    ScaleUpFactor double
    Fraction of required workers to add to Spark Standalone clusters. A scale-up factor of 1.0 will result in scaling up so that there are no more required workers for the Spark Job (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling).Bounds: 0.0, 1.0.
    ScaleUpMinWorkerFraction double
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    GracefulDecommissionTimeout string
    Timeout for Spark graceful decommissioning of spark workers. Specifies the duration to wait for spark worker to complete spark decommissioning tasks before forcefully removing workers. Only applicable to downscaling operations.Bounds: 0s, 1d.
    RemoveOnlyIdleWorkers bool
    Optional. Remove only idle workers when scaling down cluster
    ScaleDownFactor float64
    Fraction of required executors to remove from Spark Serverless clusters. A scale-down factor of 1.0 will result in scaling down so that there are no more executors for the Spark Job.(more aggressive scaling). A scale-down factor closer to 0 will result in a smaller magnitude of scaling donw (less aggressive scaling).Bounds: 0.0, 1.0.
    ScaleDownMinWorkerFraction float64
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    ScaleUpFactor float64
    Fraction of required workers to add to Spark Standalone clusters. A scale-up factor of 1.0 will result in scaling up so that there are no more required workers for the Spark Job (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling).Bounds: 0.0, 1.0.
    ScaleUpMinWorkerFraction float64
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    gracefulDecommissionTimeout String
    Timeout for Spark graceful decommissioning of spark workers. Specifies the duration to wait for spark worker to complete spark decommissioning tasks before forcefully removing workers. Only applicable to downscaling operations.Bounds: 0s, 1d.
    removeOnlyIdleWorkers Boolean
    Optional. Remove only idle workers when scaling down cluster
    scaleDownFactor Double
    Fraction of required executors to remove from Spark Serverless clusters. A scale-down factor of 1.0 will result in scaling down so that there are no more executors for the Spark Job.(more aggressive scaling). A scale-down factor closer to 0 will result in a smaller magnitude of scaling donw (less aggressive scaling).Bounds: 0.0, 1.0.
    scaleDownMinWorkerFraction Double
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    scaleUpFactor Double
    Fraction of required workers to add to Spark Standalone clusters. A scale-up factor of 1.0 will result in scaling up so that there are no more required workers for the Spark Job (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling).Bounds: 0.0, 1.0.
    scaleUpMinWorkerFraction Double
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    gracefulDecommissionTimeout string
    Timeout for Spark graceful decommissioning of spark workers. Specifies the duration to wait for spark worker to complete spark decommissioning tasks before forcefully removing workers. Only applicable to downscaling operations.Bounds: 0s, 1d.
    removeOnlyIdleWorkers boolean
    Optional. Remove only idle workers when scaling down cluster
    scaleDownFactor number
    Fraction of required executors to remove from Spark Serverless clusters. A scale-down factor of 1.0 will result in scaling down so that there are no more executors for the Spark Job.(more aggressive scaling). A scale-down factor closer to 0 will result in a smaller magnitude of scaling donw (less aggressive scaling).Bounds: 0.0, 1.0.
    scaleDownMinWorkerFraction number
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    scaleUpFactor number
    Fraction of required workers to add to Spark Standalone clusters. A scale-up factor of 1.0 will result in scaling up so that there are no more required workers for the Spark Job (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling).Bounds: 0.0, 1.0.
    scaleUpMinWorkerFraction number
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    graceful_decommission_timeout str
    Timeout for Spark graceful decommissioning of spark workers. Specifies the duration to wait for spark worker to complete spark decommissioning tasks before forcefully removing workers. Only applicable to downscaling operations.Bounds: 0s, 1d.
    remove_only_idle_workers bool
    Optional. Remove only idle workers when scaling down cluster
    scale_down_factor float
    Fraction of required executors to remove from Spark Serverless clusters. A scale-down factor of 1.0 will result in scaling down so that there are no more executors for the Spark Job.(more aggressive scaling). A scale-down factor closer to 0 will result in a smaller magnitude of scaling donw (less aggressive scaling).Bounds: 0.0, 1.0.
    scale_down_min_worker_fraction float
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    scale_up_factor float
    Fraction of required workers to add to Spark Standalone clusters. A scale-up factor of 1.0 will result in scaling up so that there are no more required workers for the Spark Job (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling).Bounds: 0.0, 1.0.
    scale_up_min_worker_fraction float
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    gracefulDecommissionTimeout String
    Timeout for Spark graceful decommissioning of spark workers. Specifies the duration to wait for spark worker to complete spark decommissioning tasks before forcefully removing workers. Only applicable to downscaling operations.Bounds: 0s, 1d.
    removeOnlyIdleWorkers Boolean
    Optional. Remove only idle workers when scaling down cluster
    scaleDownFactor Number
    Fraction of required executors to remove from Spark Serverless clusters. A scale-down factor of 1.0 will result in scaling down so that there are no more executors for the Spark Job.(more aggressive scaling). A scale-down factor closer to 0 will result in a smaller magnitude of scaling donw (less aggressive scaling).Bounds: 0.0, 1.0.
    scaleDownMinWorkerFraction Number
    Optional. Minimum scale-down threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2 worker scale-down for the cluster to scale. A threshold of 0 means the autoscaler will scale down on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.
    scaleUpFactor Number
    Fraction of required workers to add to Spark Standalone clusters. A scale-up factor of 1.0 will result in scaling up so that there are no more required workers for the Spark Job (more aggressive scaling). A scale-up factor closer to 0 will result in a smaller magnitude of scaling up (less aggressive scaling).Bounds: 0.0, 1.0.
    scaleUpMinWorkerFraction Number
    Optional. Minimum scale-up threshold as a fraction of total cluster size before scaling occurs. For example, in a 20-worker cluster, a threshold of 0.1 means the autoscaler must recommend at least a 2-worker scale-up for the cluster to scale. A threshold of 0 means the autoscaler will scale up on any recommended change.Bounds: 0.0, 1.0. Default: 0.0.

    Package Details

    Repository
    Google Cloud Native pulumi/pulumi-google-native
    License
    Apache-2.0
    google-native logo

    Google Cloud Native is in preview. Google Cloud Classic is fully supported.

    Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
    pulumi/pulumi-google-native