1. Packages
  2. Castai Provider
  3. API Docs
  4. WorkloadScalingPolicy
castai 7.43.0 published on Thursday, Mar 20, 2025 by castai

castai.WorkloadScalingPolicy

Explore with Pulumi AI

Create WorkloadScalingPolicy Resource

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

Constructor syntax

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

@overload
def WorkloadScalingPolicy(resource_name: str,
                          opts: Optional[ResourceOptions] = None,
                          management_option: Optional[str] = None,
                          apply_type: Optional[str] = None,
                          cluster_id: Optional[str] = None,
                          memory: Optional[WorkloadScalingPolicyMemoryArgs] = None,
                          cpu: Optional[WorkloadScalingPolicyCpuArgs] = None,
                          confidence: Optional[WorkloadScalingPolicyConfidenceArgs] = None,
                          downscaling: Optional[WorkloadScalingPolicyDownscalingArgs] = None,
                          anti_affinity: Optional[WorkloadScalingPolicyAntiAffinityArgs] = None,
                          memory_event: Optional[WorkloadScalingPolicyMemoryEventArgs] = None,
                          name: Optional[str] = None,
                          startup: Optional[WorkloadScalingPolicyStartupArgs] = None,
                          timeouts: Optional[WorkloadScalingPolicyTimeoutsArgs] = None,
                          workload_scaling_policy_id: Optional[str] = None)
func NewWorkloadScalingPolicy(ctx *Context, name string, args WorkloadScalingPolicyArgs, opts ...ResourceOption) (*WorkloadScalingPolicy, error)
public WorkloadScalingPolicy(string name, WorkloadScalingPolicyArgs args, CustomResourceOptions? opts = null)
public WorkloadScalingPolicy(String name, WorkloadScalingPolicyArgs args)
public WorkloadScalingPolicy(String name, WorkloadScalingPolicyArgs args, CustomResourceOptions options)
type: castai:WorkloadScalingPolicy
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

Parameters

name This property is required. string
The unique name of the resource.
args This property is required. WorkloadScalingPolicyArgs
The arguments to resource properties.
opts CustomResourceOptions
Bag of options to control resource's behavior.
resource_name This property is required. str
The unique name of the resource.
args This property is required. WorkloadScalingPolicyArgs
The arguments to resource properties.
opts ResourceOptions
Bag of options to control resource's behavior.
ctx Context
Context object for the current deployment.
name This property is required. string
The unique name of the resource.
args This property is required. WorkloadScalingPolicyArgs
The arguments to resource properties.
opts ResourceOption
Bag of options to control resource's behavior.
name This property is required. string
The unique name of the resource.
args This property is required. WorkloadScalingPolicyArgs
The arguments to resource properties.
opts CustomResourceOptions
Bag of options to control resource's behavior.
name This property is required. String
The unique name of the resource.
args This property is required. WorkloadScalingPolicyArgs
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 workloadScalingPolicyResource = new Castai.WorkloadScalingPolicy("workloadScalingPolicyResource", new()
{
    ManagementOption = "string",
    ApplyType = "string",
    ClusterId = "string",
    Memory = new Castai.Inputs.WorkloadScalingPolicyMemoryArgs
    {
        ApplyThresholdStrategy = new Castai.Inputs.WorkloadScalingPolicyMemoryApplyThresholdStrategyArgs
        {
            Type = "string",
            Denominator = "string",
            Exponent = 0,
            Numerator = 0,
            Percentage = 0,
        },
        Args = new[]
        {
            "string",
        },
        Function = "string",
        Limit = new Castai.Inputs.WorkloadScalingPolicyMemoryLimitArgs
        {
            Type = "string",
            Multiplier = 0,
        },
        LookBackPeriodSeconds = 0,
        ManagementOption = "string",
        Max = 0,
        Min = 0,
        Overhead = 0,
    },
    Cpu = new Castai.Inputs.WorkloadScalingPolicyCpuArgs
    {
        ApplyThresholdStrategy = new Castai.Inputs.WorkloadScalingPolicyCpuApplyThresholdStrategyArgs
        {
            Type = "string",
            Denominator = "string",
            Exponent = 0,
            Numerator = 0,
            Percentage = 0,
        },
        Args = new[]
        {
            "string",
        },
        Function = "string",
        Limit = new Castai.Inputs.WorkloadScalingPolicyCpuLimitArgs
        {
            Type = "string",
            Multiplier = 0,
        },
        LookBackPeriodSeconds = 0,
        ManagementOption = "string",
        Max = 0,
        Min = 0,
        Overhead = 0,
    },
    Confidence = new Castai.Inputs.WorkloadScalingPolicyConfidenceArgs
    {
        Threshold = 0,
    },
    Downscaling = new Castai.Inputs.WorkloadScalingPolicyDownscalingArgs
    {
        ApplyType = "string",
    },
    AntiAffinity = new Castai.Inputs.WorkloadScalingPolicyAntiAffinityArgs
    {
        ConsiderAntiAffinity = false,
    },
    MemoryEvent = new Castai.Inputs.WorkloadScalingPolicyMemoryEventArgs
    {
        ApplyType = "string",
    },
    Name = "string",
    Startup = new Castai.Inputs.WorkloadScalingPolicyStartupArgs
    {
        PeriodSeconds = 0,
    },
    Timeouts = new Castai.Inputs.WorkloadScalingPolicyTimeoutsArgs
    {
        Create = "string",
        Delete = "string",
        Read = "string",
        Update = "string",
    },
    WorkloadScalingPolicyId = "string",
});
Copy
example, err := castai.NewWorkloadScalingPolicy(ctx, "workloadScalingPolicyResource", &castai.WorkloadScalingPolicyArgs{
ManagementOption: pulumi.String("string"),
ApplyType: pulumi.String("string"),
ClusterId: pulumi.String("string"),
Memory: &.WorkloadScalingPolicyMemoryArgs{
ApplyThresholdStrategy: &.WorkloadScalingPolicyMemoryApplyThresholdStrategyArgs{
Type: pulumi.String("string"),
Denominator: pulumi.String("string"),
Exponent: pulumi.Float64(0),
Numerator: pulumi.Float64(0),
Percentage: pulumi.Float64(0),
},
Args: pulumi.StringArray{
pulumi.String("string"),
},
Function: pulumi.String("string"),
Limit: &.WorkloadScalingPolicyMemoryLimitArgs{
Type: pulumi.String("string"),
Multiplier: pulumi.Float64(0),
},
LookBackPeriodSeconds: pulumi.Float64(0),
ManagementOption: pulumi.String("string"),
Max: pulumi.Float64(0),
Min: pulumi.Float64(0),
Overhead: pulumi.Float64(0),
},
Cpu: &.WorkloadScalingPolicyCpuArgs{
ApplyThresholdStrategy: &.WorkloadScalingPolicyCpuApplyThresholdStrategyArgs{
Type: pulumi.String("string"),
Denominator: pulumi.String("string"),
Exponent: pulumi.Float64(0),
Numerator: pulumi.Float64(0),
Percentage: pulumi.Float64(0),
},
Args: pulumi.StringArray{
pulumi.String("string"),
},
Function: pulumi.String("string"),
Limit: &.WorkloadScalingPolicyCpuLimitArgs{
Type: pulumi.String("string"),
Multiplier: pulumi.Float64(0),
},
LookBackPeriodSeconds: pulumi.Float64(0),
ManagementOption: pulumi.String("string"),
Max: pulumi.Float64(0),
Min: pulumi.Float64(0),
Overhead: pulumi.Float64(0),
},
Confidence: &.WorkloadScalingPolicyConfidenceArgs{
Threshold: pulumi.Float64(0),
},
Downscaling: &.WorkloadScalingPolicyDownscalingArgs{
ApplyType: pulumi.String("string"),
},
AntiAffinity: &.WorkloadScalingPolicyAntiAffinityArgs{
ConsiderAntiAffinity: pulumi.Bool(false),
},
MemoryEvent: &.WorkloadScalingPolicyMemoryEventArgs{
ApplyType: pulumi.String("string"),
},
Name: pulumi.String("string"),
Startup: &.WorkloadScalingPolicyStartupArgs{
PeriodSeconds: pulumi.Float64(0),
},
Timeouts: &.WorkloadScalingPolicyTimeoutsArgs{
Create: pulumi.String("string"),
Delete: pulumi.String("string"),
Read: pulumi.String("string"),
Update: pulumi.String("string"),
},
WorkloadScalingPolicyId: pulumi.String("string"),
})
Copy
var workloadScalingPolicyResource = new WorkloadScalingPolicy("workloadScalingPolicyResource", WorkloadScalingPolicyArgs.builder()
    .managementOption("string")
    .applyType("string")
    .clusterId("string")
    .memory(WorkloadScalingPolicyMemoryArgs.builder()
        .applyThresholdStrategy(WorkloadScalingPolicyMemoryApplyThresholdStrategyArgs.builder()
            .type("string")
            .denominator("string")
            .exponent(0)
            .numerator(0)
            .percentage(0)
            .build())
        .args("string")
        .function("string")
        .limit(WorkloadScalingPolicyMemoryLimitArgs.builder()
            .type("string")
            .multiplier(0)
            .build())
        .lookBackPeriodSeconds(0)
        .managementOption("string")
        .max(0)
        .min(0)
        .overhead(0)
        .build())
    .cpu(WorkloadScalingPolicyCpuArgs.builder()
        .applyThresholdStrategy(WorkloadScalingPolicyCpuApplyThresholdStrategyArgs.builder()
            .type("string")
            .denominator("string")
            .exponent(0)
            .numerator(0)
            .percentage(0)
            .build())
        .args("string")
        .function("string")
        .limit(WorkloadScalingPolicyCpuLimitArgs.builder()
            .type("string")
            .multiplier(0)
            .build())
        .lookBackPeriodSeconds(0)
        .managementOption("string")
        .max(0)
        .min(0)
        .overhead(0)
        .build())
    .confidence(WorkloadScalingPolicyConfidenceArgs.builder()
        .threshold(0)
        .build())
    .downscaling(WorkloadScalingPolicyDownscalingArgs.builder()
        .applyType("string")
        .build())
    .antiAffinity(WorkloadScalingPolicyAntiAffinityArgs.builder()
        .considerAntiAffinity(false)
        .build())
    .memoryEvent(WorkloadScalingPolicyMemoryEventArgs.builder()
        .applyType("string")
        .build())
    .name("string")
    .startup(WorkloadScalingPolicyStartupArgs.builder()
        .periodSeconds(0)
        .build())
    .timeouts(WorkloadScalingPolicyTimeoutsArgs.builder()
        .create("string")
        .delete("string")
        .read("string")
        .update("string")
        .build())
    .workloadScalingPolicyId("string")
    .build());
Copy
workload_scaling_policy_resource = castai.WorkloadScalingPolicy("workloadScalingPolicyResource",
    management_option="string",
    apply_type="string",
    cluster_id="string",
    memory={
        "apply_threshold_strategy": {
            "type": "string",
            "denominator": "string",
            "exponent": 0,
            "numerator": 0,
            "percentage": 0,
        },
        "args": ["string"],
        "function": "string",
        "limit": {
            "type": "string",
            "multiplier": 0,
        },
        "look_back_period_seconds": 0,
        "management_option": "string",
        "max": 0,
        "min": 0,
        "overhead": 0,
    },
    cpu={
        "apply_threshold_strategy": {
            "type": "string",
            "denominator": "string",
            "exponent": 0,
            "numerator": 0,
            "percentage": 0,
        },
        "args": ["string"],
        "function": "string",
        "limit": {
            "type": "string",
            "multiplier": 0,
        },
        "look_back_period_seconds": 0,
        "management_option": "string",
        "max": 0,
        "min": 0,
        "overhead": 0,
    },
    confidence={
        "threshold": 0,
    },
    downscaling={
        "apply_type": "string",
    },
    anti_affinity={
        "consider_anti_affinity": False,
    },
    memory_event={
        "apply_type": "string",
    },
    name="string",
    startup={
        "period_seconds": 0,
    },
    timeouts={
        "create": "string",
        "delete": "string",
        "read": "string",
        "update": "string",
    },
    workload_scaling_policy_id="string")
Copy
const workloadScalingPolicyResource = new castai.WorkloadScalingPolicy("workloadScalingPolicyResource", {
    managementOption: "string",
    applyType: "string",
    clusterId: "string",
    memory: {
        applyThresholdStrategy: {
            type: "string",
            denominator: "string",
            exponent: 0,
            numerator: 0,
            percentage: 0,
        },
        args: ["string"],
        "function": "string",
        limit: {
            type: "string",
            multiplier: 0,
        },
        lookBackPeriodSeconds: 0,
        managementOption: "string",
        max: 0,
        min: 0,
        overhead: 0,
    },
    cpu: {
        applyThresholdStrategy: {
            type: "string",
            denominator: "string",
            exponent: 0,
            numerator: 0,
            percentage: 0,
        },
        args: ["string"],
        "function": "string",
        limit: {
            type: "string",
            multiplier: 0,
        },
        lookBackPeriodSeconds: 0,
        managementOption: "string",
        max: 0,
        min: 0,
        overhead: 0,
    },
    confidence: {
        threshold: 0,
    },
    downscaling: {
        applyType: "string",
    },
    antiAffinity: {
        considerAntiAffinity: false,
    },
    memoryEvent: {
        applyType: "string",
    },
    name: "string",
    startup: {
        periodSeconds: 0,
    },
    timeouts: {
        create: "string",
        "delete": "string",
        read: "string",
        update: "string",
    },
    workloadScalingPolicyId: "string",
});
Copy
type: castai:WorkloadScalingPolicy
properties:
    antiAffinity:
        considerAntiAffinity: false
    applyType: string
    clusterId: string
    confidence:
        threshold: 0
    cpu:
        applyThresholdStrategy:
            denominator: string
            exponent: 0
            numerator: 0
            percentage: 0
            type: string
        args:
            - string
        function: string
        limit:
            multiplier: 0
            type: string
        lookBackPeriodSeconds: 0
        managementOption: string
        max: 0
        min: 0
        overhead: 0
    downscaling:
        applyType: string
    managementOption: string
    memory:
        applyThresholdStrategy:
            denominator: string
            exponent: 0
            numerator: 0
            percentage: 0
            type: string
        args:
            - string
        function: string
        limit:
            multiplier: 0
            type: string
        lookBackPeriodSeconds: 0
        managementOption: string
        max: 0
        min: 0
        overhead: 0
    memoryEvent:
        applyType: string
    name: string
    startup:
        periodSeconds: 0
    timeouts:
        create: string
        delete: string
        read: string
        update: string
    workloadScalingPolicyId: string
Copy

WorkloadScalingPolicy 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 WorkloadScalingPolicy resource accepts the following input properties:

ApplyType This property is required. string
Recommendation apply type. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED

  • pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
ClusterId This property is required. string
CAST AI cluster id
Cpu This property is required. WorkloadScalingPolicyCpu
ManagementOption This property is required. string
Defines possible options for workload management. - READ_ONLY - workload watched (metrics collected), but no actions performed by CAST AI. - MANAGED - workload watched (metrics collected), CAST AI may perform actions on the workload.
Memory This property is required. WorkloadScalingPolicyMemory
AntiAffinity WorkloadScalingPolicyAntiAffinity
Confidence WorkloadScalingPolicyConfidence
Defines the confidence settings for applying recommendations.
Downscaling WorkloadScalingPolicyDownscaling
MemoryEvent WorkloadScalingPolicyMemoryEvent
Name string
Scaling policy name
Startup WorkloadScalingPolicyStartup
Timeouts WorkloadScalingPolicyTimeouts
WorkloadScalingPolicyId string
ApplyType This property is required. string
Recommendation apply type. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED

  • pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
ClusterId This property is required. string
CAST AI cluster id
Cpu This property is required. WorkloadScalingPolicyCpuArgs
ManagementOption This property is required. string
Defines possible options for workload management. - READ_ONLY - workload watched (metrics collected), but no actions performed by CAST AI. - MANAGED - workload watched (metrics collected), CAST AI may perform actions on the workload.
Memory This property is required. WorkloadScalingPolicyMemoryArgs
AntiAffinity WorkloadScalingPolicyAntiAffinityArgs
Confidence WorkloadScalingPolicyConfidenceArgs
Defines the confidence settings for applying recommendations.
Downscaling WorkloadScalingPolicyDownscalingArgs
MemoryEvent WorkloadScalingPolicyMemoryEventArgs
Name string
Scaling policy name
Startup WorkloadScalingPolicyStartupArgs
Timeouts WorkloadScalingPolicyTimeoutsArgs
WorkloadScalingPolicyId string
applyType This property is required. String
Recommendation apply type. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED

  • pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
clusterId This property is required. String
CAST AI cluster id
cpu This property is required. WorkloadScalingPolicyCpu
managementOption This property is required. String
Defines possible options for workload management. - READ_ONLY - workload watched (metrics collected), but no actions performed by CAST AI. - MANAGED - workload watched (metrics collected), CAST AI may perform actions on the workload.
memory This property is required. WorkloadScalingPolicyMemory
antiAffinity WorkloadScalingPolicyAntiAffinity
confidence WorkloadScalingPolicyConfidence
Defines the confidence settings for applying recommendations.
downscaling WorkloadScalingPolicyDownscaling
memoryEvent WorkloadScalingPolicyMemoryEvent
name String
Scaling policy name
startup WorkloadScalingPolicyStartup
timeouts WorkloadScalingPolicyTimeouts
workloadScalingPolicyId String
applyType This property is required. string
Recommendation apply type. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED

  • pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
clusterId This property is required. string
CAST AI cluster id
cpu This property is required. WorkloadScalingPolicyCpu
managementOption This property is required. string
Defines possible options for workload management. - READ_ONLY - workload watched (metrics collected), but no actions performed by CAST AI. - MANAGED - workload watched (metrics collected), CAST AI may perform actions on the workload.
memory This property is required. WorkloadScalingPolicyMemory
antiAffinity WorkloadScalingPolicyAntiAffinity
confidence WorkloadScalingPolicyConfidence
Defines the confidence settings for applying recommendations.
downscaling WorkloadScalingPolicyDownscaling
memoryEvent WorkloadScalingPolicyMemoryEvent
name string
Scaling policy name
startup WorkloadScalingPolicyStartup
timeouts WorkloadScalingPolicyTimeouts
workloadScalingPolicyId string
apply_type This property is required. str
Recommendation apply type. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED

  • pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
cluster_id This property is required. str
CAST AI cluster id
cpu This property is required. WorkloadScalingPolicyCpuArgs
management_option This property is required. str
Defines possible options for workload management. - READ_ONLY - workload watched (metrics collected), but no actions performed by CAST AI. - MANAGED - workload watched (metrics collected), CAST AI may perform actions on the workload.
memory This property is required. WorkloadScalingPolicyMemoryArgs
anti_affinity WorkloadScalingPolicyAntiAffinityArgs
confidence WorkloadScalingPolicyConfidenceArgs
Defines the confidence settings for applying recommendations.
downscaling WorkloadScalingPolicyDownscalingArgs
memory_event WorkloadScalingPolicyMemoryEventArgs
name str
Scaling policy name
startup WorkloadScalingPolicyStartupArgs
timeouts WorkloadScalingPolicyTimeoutsArgs
workload_scaling_policy_id str
applyType This property is required. String
Recommendation apply type. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED

  • pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
clusterId This property is required. String
CAST AI cluster id
cpu This property is required. Property Map
managementOption This property is required. String
Defines possible options for workload management. - READ_ONLY - workload watched (metrics collected), but no actions performed by CAST AI. - MANAGED - workload watched (metrics collected), CAST AI may perform actions on the workload.
memory This property is required. Property Map
antiAffinity Property Map
confidence Property Map
Defines the confidence settings for applying recommendations.
downscaling Property Map
memoryEvent Property Map
name String
Scaling policy name
startup Property Map
timeouts Property Map
workloadScalingPolicyId String

Outputs

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

Id string
The provider-assigned unique ID for this managed resource.
Id string
The provider-assigned unique ID for this managed resource.
id String
The provider-assigned unique ID for this managed resource.
id string
The provider-assigned unique ID for this managed resource.
id str
The provider-assigned unique ID for this managed resource.
id String
The provider-assigned unique ID for this managed resource.

Look up Existing WorkloadScalingPolicy Resource

Get an existing WorkloadScalingPolicy resource’s state with the given name, ID, and optional extra properties used to qualify the lookup.

public static get(name: string, id: Input<ID>, state?: WorkloadScalingPolicyState, opts?: CustomResourceOptions): WorkloadScalingPolicy
@staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        anti_affinity: Optional[WorkloadScalingPolicyAntiAffinityArgs] = None,
        apply_type: Optional[str] = None,
        cluster_id: Optional[str] = None,
        confidence: Optional[WorkloadScalingPolicyConfidenceArgs] = None,
        cpu: Optional[WorkloadScalingPolicyCpuArgs] = None,
        downscaling: Optional[WorkloadScalingPolicyDownscalingArgs] = None,
        management_option: Optional[str] = None,
        memory: Optional[WorkloadScalingPolicyMemoryArgs] = None,
        memory_event: Optional[WorkloadScalingPolicyMemoryEventArgs] = None,
        name: Optional[str] = None,
        startup: Optional[WorkloadScalingPolicyStartupArgs] = None,
        timeouts: Optional[WorkloadScalingPolicyTimeoutsArgs] = None,
        workload_scaling_policy_id: Optional[str] = None) -> WorkloadScalingPolicy
func GetWorkloadScalingPolicy(ctx *Context, name string, id IDInput, state *WorkloadScalingPolicyState, opts ...ResourceOption) (*WorkloadScalingPolicy, error)
public static WorkloadScalingPolicy Get(string name, Input<string> id, WorkloadScalingPolicyState? state, CustomResourceOptions? opts = null)
public static WorkloadScalingPolicy get(String name, Output<String> id, WorkloadScalingPolicyState state, CustomResourceOptions options)
resources:  _:    type: castai:WorkloadScalingPolicy    get:      id: ${id}
name This property is required.
The unique name of the resulting resource.
id This property is required.
The unique provider ID of the resource to lookup.
state
Any extra arguments used during the lookup.
opts
A bag of options that control this resource's behavior.
resource_name This property is required.
The unique name of the resulting resource.
id This property is required.
The unique provider ID of the resource to lookup.
name This property is required.
The unique name of the resulting resource.
id This property is required.
The unique provider ID of the resource to lookup.
state
Any extra arguments used during the lookup.
opts
A bag of options that control this resource's behavior.
name This property is required.
The unique name of the resulting resource.
id This property is required.
The unique provider ID of the resource to lookup.
state
Any extra arguments used during the lookup.
opts
A bag of options that control this resource's behavior.
name This property is required.
The unique name of the resulting resource.
id This property is required.
The unique provider ID of the resource to lookup.
state
Any extra arguments used during the lookup.
opts
A bag of options that control this resource's behavior.
The following state arguments are supported:
AntiAffinity WorkloadScalingPolicyAntiAffinity
ApplyType string
Recommendation apply type. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED

  • pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
ClusterId string
CAST AI cluster id
Confidence WorkloadScalingPolicyConfidence
Defines the confidence settings for applying recommendations.
Cpu WorkloadScalingPolicyCpu
Downscaling WorkloadScalingPolicyDownscaling
ManagementOption string
Defines possible options for workload management. - READ_ONLY - workload watched (metrics collected), but no actions performed by CAST AI. - MANAGED - workload watched (metrics collected), CAST AI may perform actions on the workload.
Memory WorkloadScalingPolicyMemory
MemoryEvent WorkloadScalingPolicyMemoryEvent
Name string
Scaling policy name
Startup WorkloadScalingPolicyStartup
Timeouts WorkloadScalingPolicyTimeouts
WorkloadScalingPolicyId string
AntiAffinity WorkloadScalingPolicyAntiAffinityArgs
ApplyType string
Recommendation apply type. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED

  • pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
ClusterId string
CAST AI cluster id
Confidence WorkloadScalingPolicyConfidenceArgs
Defines the confidence settings for applying recommendations.
Cpu WorkloadScalingPolicyCpuArgs
Downscaling WorkloadScalingPolicyDownscalingArgs
ManagementOption string
Defines possible options for workload management. - READ_ONLY - workload watched (metrics collected), but no actions performed by CAST AI. - MANAGED - workload watched (metrics collected), CAST AI may perform actions on the workload.
Memory WorkloadScalingPolicyMemoryArgs
MemoryEvent WorkloadScalingPolicyMemoryEventArgs
Name string
Scaling policy name
Startup WorkloadScalingPolicyStartupArgs
Timeouts WorkloadScalingPolicyTimeoutsArgs
WorkloadScalingPolicyId string
antiAffinity WorkloadScalingPolicyAntiAffinity
applyType String
Recommendation apply type. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED

  • pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
clusterId String
CAST AI cluster id
confidence WorkloadScalingPolicyConfidence
Defines the confidence settings for applying recommendations.
cpu WorkloadScalingPolicyCpu
downscaling WorkloadScalingPolicyDownscaling
managementOption String
Defines possible options for workload management. - READ_ONLY - workload watched (metrics collected), but no actions performed by CAST AI. - MANAGED - workload watched (metrics collected), CAST AI may perform actions on the workload.
memory WorkloadScalingPolicyMemory
memoryEvent WorkloadScalingPolicyMemoryEvent
name String
Scaling policy name
startup WorkloadScalingPolicyStartup
timeouts WorkloadScalingPolicyTimeouts
workloadScalingPolicyId String
antiAffinity WorkloadScalingPolicyAntiAffinity
applyType string
Recommendation apply type. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED

  • pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
clusterId string
CAST AI cluster id
confidence WorkloadScalingPolicyConfidence
Defines the confidence settings for applying recommendations.
cpu WorkloadScalingPolicyCpu
downscaling WorkloadScalingPolicyDownscaling
managementOption string
Defines possible options for workload management. - READ_ONLY - workload watched (metrics collected), but no actions performed by CAST AI. - MANAGED - workload watched (metrics collected), CAST AI may perform actions on the workload.
memory WorkloadScalingPolicyMemory
memoryEvent WorkloadScalingPolicyMemoryEvent
name string
Scaling policy name
startup WorkloadScalingPolicyStartup
timeouts WorkloadScalingPolicyTimeouts
workloadScalingPolicyId string
anti_affinity WorkloadScalingPolicyAntiAffinityArgs
apply_type str
Recommendation apply type. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED

  • pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
cluster_id str
CAST AI cluster id
confidence WorkloadScalingPolicyConfidenceArgs
Defines the confidence settings for applying recommendations.
cpu WorkloadScalingPolicyCpuArgs
downscaling WorkloadScalingPolicyDownscalingArgs
management_option str
Defines possible options for workload management. - READ_ONLY - workload watched (metrics collected), but no actions performed by CAST AI. - MANAGED - workload watched (metrics collected), CAST AI may perform actions on the workload.
memory WorkloadScalingPolicyMemoryArgs
memory_event WorkloadScalingPolicyMemoryEventArgs
name str
Scaling policy name
startup WorkloadScalingPolicyStartupArgs
timeouts WorkloadScalingPolicyTimeoutsArgs
workload_scaling_policy_id str
antiAffinity Property Map
applyType String
Recommendation apply type. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED

  • pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
clusterId String
CAST AI cluster id
confidence Property Map
Defines the confidence settings for applying recommendations.
cpu Property Map
downscaling Property Map
managementOption String
Defines possible options for workload management. - READ_ONLY - workload watched (metrics collected), but no actions performed by CAST AI. - MANAGED - workload watched (metrics collected), CAST AI may perform actions on the workload.
memory Property Map
memoryEvent Property Map
name String
Scaling policy name
startup Property Map
timeouts Property Map
workloadScalingPolicyId String

Supporting Types

WorkloadScalingPolicyAntiAffinity
, WorkloadScalingPolicyAntiAffinityArgs

ConsiderAntiAffinity bool
Defines if anti-affinity should be considered when scaling the workload. If enabled, requiring host ports, or having anti-affinity on hostname will force all recommendations to be deferred.
ConsiderAntiAffinity bool
Defines if anti-affinity should be considered when scaling the workload. If enabled, requiring host ports, or having anti-affinity on hostname will force all recommendations to be deferred.
considerAntiAffinity Boolean
Defines if anti-affinity should be considered when scaling the workload. If enabled, requiring host ports, or having anti-affinity on hostname will force all recommendations to be deferred.
considerAntiAffinity boolean
Defines if anti-affinity should be considered when scaling the workload. If enabled, requiring host ports, or having anti-affinity on hostname will force all recommendations to be deferred.
consider_anti_affinity bool
Defines if anti-affinity should be considered when scaling the workload. If enabled, requiring host ports, or having anti-affinity on hostname will force all recommendations to be deferred.
considerAntiAffinity Boolean
Defines if anti-affinity should be considered when scaling the workload. If enabled, requiring host ports, or having anti-affinity on hostname will force all recommendations to be deferred.

WorkloadScalingPolicyConfidence
, WorkloadScalingPolicyConfidenceArgs

Threshold double
Defines the confidence threshold for applying recommendations. The smaller number indicates that we require fewer metrics data points to apply recommendations - changing this value can cause applying less precise recommendations. Do not change the default unless you want to optimize with fewer data points (e.g., short-lived workloads).
Threshold float64
Defines the confidence threshold for applying recommendations. The smaller number indicates that we require fewer metrics data points to apply recommendations - changing this value can cause applying less precise recommendations. Do not change the default unless you want to optimize with fewer data points (e.g., short-lived workloads).
threshold Double
Defines the confidence threshold for applying recommendations. The smaller number indicates that we require fewer metrics data points to apply recommendations - changing this value can cause applying less precise recommendations. Do not change the default unless you want to optimize with fewer data points (e.g., short-lived workloads).
threshold number
Defines the confidence threshold for applying recommendations. The smaller number indicates that we require fewer metrics data points to apply recommendations - changing this value can cause applying less precise recommendations. Do not change the default unless you want to optimize with fewer data points (e.g., short-lived workloads).
threshold float
Defines the confidence threshold for applying recommendations. The smaller number indicates that we require fewer metrics data points to apply recommendations - changing this value can cause applying less precise recommendations. Do not change the default unless you want to optimize with fewer data points (e.g., short-lived workloads).
threshold Number
Defines the confidence threshold for applying recommendations. The smaller number indicates that we require fewer metrics data points to apply recommendations - changing this value can cause applying less precise recommendations. Do not change the default unless you want to optimize with fewer data points (e.g., short-lived workloads).

WorkloadScalingPolicyCpu
, WorkloadScalingPolicyCpuArgs

ApplyThreshold double
The threshold of when to apply the recommendation. Recommendation will be applied when diff of current requests and new recommendation is greater than set value

Deprecated: Deprecated

ApplyThresholdStrategy WorkloadScalingPolicyCpuApplyThresholdStrategy
Resource apply threshold strategy settings. The default strategy is PERCENTAGE with percentage value set to 0.1.
Args List<string>
The arguments for the function - i.e. for QUANTILE this should be a [0, 1] float. MAX doesn't accept any args
Function string
The function used to calculate the resource recommendation. Supported values: QUANTILE, MAX
Limit WorkloadScalingPolicyCpuLimit
Resource limit settings
LookBackPeriodSeconds double
The look back period in seconds for the recommendation.
ManagementOption string
Disables management for a single resource when set to READ_ONLY. The resource will use its original workload template requests and limits. Supported value: READ_ONLY. Minimum required workload-autoscaler version: v0.23.1.
Max double
Max values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
Min double
Min values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
Overhead double
Overhead for the recommendation, e.g. 0.1 will result in 10% higher recommendation
ApplyThreshold float64
The threshold of when to apply the recommendation. Recommendation will be applied when diff of current requests and new recommendation is greater than set value

Deprecated: Deprecated

ApplyThresholdStrategy WorkloadScalingPolicyCpuApplyThresholdStrategy
Resource apply threshold strategy settings. The default strategy is PERCENTAGE with percentage value set to 0.1.
Args []string
The arguments for the function - i.e. for QUANTILE this should be a [0, 1] float. MAX doesn't accept any args
Function string
The function used to calculate the resource recommendation. Supported values: QUANTILE, MAX
Limit WorkloadScalingPolicyCpuLimit
Resource limit settings
LookBackPeriodSeconds float64
The look back period in seconds for the recommendation.
ManagementOption string
Disables management for a single resource when set to READ_ONLY. The resource will use its original workload template requests and limits. Supported value: READ_ONLY. Minimum required workload-autoscaler version: v0.23.1.
Max float64
Max values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
Min float64
Min values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
Overhead float64
Overhead for the recommendation, e.g. 0.1 will result in 10% higher recommendation
applyThreshold Double
The threshold of when to apply the recommendation. Recommendation will be applied when diff of current requests and new recommendation is greater than set value

Deprecated: Deprecated

applyThresholdStrategy WorkloadScalingPolicyCpuApplyThresholdStrategy
Resource apply threshold strategy settings. The default strategy is PERCENTAGE with percentage value set to 0.1.
args List<String>
The arguments for the function - i.e. for QUANTILE this should be a [0, 1] float. MAX doesn't accept any args
function String
The function used to calculate the resource recommendation. Supported values: QUANTILE, MAX
limit WorkloadScalingPolicyCpuLimit
Resource limit settings
lookBackPeriodSeconds Double
The look back period in seconds for the recommendation.
managementOption String
Disables management for a single resource when set to READ_ONLY. The resource will use its original workload template requests and limits. Supported value: READ_ONLY. Minimum required workload-autoscaler version: v0.23.1.
max Double
Max values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
min Double
Min values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
overhead Double
Overhead for the recommendation, e.g. 0.1 will result in 10% higher recommendation
applyThreshold number
The threshold of when to apply the recommendation. Recommendation will be applied when diff of current requests and new recommendation is greater than set value

Deprecated: Deprecated

applyThresholdStrategy WorkloadScalingPolicyCpuApplyThresholdStrategy
Resource apply threshold strategy settings. The default strategy is PERCENTAGE with percentage value set to 0.1.
args string[]
The arguments for the function - i.e. for QUANTILE this should be a [0, 1] float. MAX doesn't accept any args
function string
The function used to calculate the resource recommendation. Supported values: QUANTILE, MAX
limit WorkloadScalingPolicyCpuLimit
Resource limit settings
lookBackPeriodSeconds number
The look back period in seconds for the recommendation.
managementOption string
Disables management for a single resource when set to READ_ONLY. The resource will use its original workload template requests and limits. Supported value: READ_ONLY. Minimum required workload-autoscaler version: v0.23.1.
max number
Max values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
min number
Min values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
overhead number
Overhead for the recommendation, e.g. 0.1 will result in 10% higher recommendation
apply_threshold float
The threshold of when to apply the recommendation. Recommendation will be applied when diff of current requests and new recommendation is greater than set value

Deprecated: Deprecated

apply_threshold_strategy WorkloadScalingPolicyCpuApplyThresholdStrategy
Resource apply threshold strategy settings. The default strategy is PERCENTAGE with percentage value set to 0.1.
args Sequence[str]
The arguments for the function - i.e. for QUANTILE this should be a [0, 1] float. MAX doesn't accept any args
function str
The function used to calculate the resource recommendation. Supported values: QUANTILE, MAX
limit WorkloadScalingPolicyCpuLimit
Resource limit settings
look_back_period_seconds float
The look back period in seconds for the recommendation.
management_option str
Disables management for a single resource when set to READ_ONLY. The resource will use its original workload template requests and limits. Supported value: READ_ONLY. Minimum required workload-autoscaler version: v0.23.1.
max float
Max values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
min float
Min values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
overhead float
Overhead for the recommendation, e.g. 0.1 will result in 10% higher recommendation
applyThreshold Number
The threshold of when to apply the recommendation. Recommendation will be applied when diff of current requests and new recommendation is greater than set value

Deprecated: Deprecated

applyThresholdStrategy Property Map
Resource apply threshold strategy settings. The default strategy is PERCENTAGE with percentage value set to 0.1.
args List<String>
The arguments for the function - i.e. for QUANTILE this should be a [0, 1] float. MAX doesn't accept any args
function String
The function used to calculate the resource recommendation. Supported values: QUANTILE, MAX
limit Property Map
Resource limit settings
lookBackPeriodSeconds Number
The look back period in seconds for the recommendation.
managementOption String
Disables management for a single resource when set to READ_ONLY. The resource will use its original workload template requests and limits. Supported value: READ_ONLY. Minimum required workload-autoscaler version: v0.23.1.
max Number
Max values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
min Number
Min values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
overhead Number
Overhead for the recommendation, e.g. 0.1 will result in 10% higher recommendation

WorkloadScalingPolicyCpuApplyThresholdStrategy
, WorkloadScalingPolicyCpuApplyThresholdStrategyArgs

Type This property is required. string
Defines apply theshold strategy type. - PERCENTAGE - recommendation will be applied when diff of current requests and new recommendation is greater than set value - DEFAULT_ADAPTIVE - will pick larger threshold percentage for small workloads and smaller percentage for large workloads. - CUSTOM_ADAPTIVE - works in same way as DEFAULT_ADAPTIVE, but it allows to tweak parameters of adaptive threshold formula: percentage = numerator/(currentRequest + denominator)^exponent. This strategy is for advance use cases, we recommend to use DEFAULT_ADAPTIVE strategy.
Denominator string
If denominator is close or equal to 0, the threshold will be much bigger for small values.For example when numerator, exponent is 1 and denominator is 0 the threshold for 0.5 req. CPU will be 200%.It must be defined for the CUSTOM_ADAPTIVE strategy.
Exponent double
The exponent changes how fast the curve is going down. The smaller value will cause that we won’t pick extremely small number for big resources, for example: - if numerator is 0, denominator is 1, and exponent is 1, for 50 CPU we will pick 2% threshold - if numerator is 0, denominator is 1, and exponent is 0.8, for 50 CPU we will pick 4.3% threshold It must be defined for the CUSTOM_ADAPTIVE strategy.
Numerator double
The numerator affects vertical stretch of function used in adaptive threshold - smaller number will create smaller threshold.It must be defined for the CUSTOM_ADAPTIVE strategy.
Percentage double
Percentage of a how much difference should there be between the current pod requests and the new recommendation. It must be defined for the PERCENTAGE strategy.
Type This property is required. string
Defines apply theshold strategy type. - PERCENTAGE - recommendation will be applied when diff of current requests and new recommendation is greater than set value - DEFAULT_ADAPTIVE - will pick larger threshold percentage for small workloads and smaller percentage for large workloads. - CUSTOM_ADAPTIVE - works in same way as DEFAULT_ADAPTIVE, but it allows to tweak parameters of adaptive threshold formula: percentage = numerator/(currentRequest + denominator)^exponent. This strategy is for advance use cases, we recommend to use DEFAULT_ADAPTIVE strategy.
Denominator string
If denominator is close or equal to 0, the threshold will be much bigger for small values.For example when numerator, exponent is 1 and denominator is 0 the threshold for 0.5 req. CPU will be 200%.It must be defined for the CUSTOM_ADAPTIVE strategy.
Exponent float64
The exponent changes how fast the curve is going down. The smaller value will cause that we won’t pick extremely small number for big resources, for example: - if numerator is 0, denominator is 1, and exponent is 1, for 50 CPU we will pick 2% threshold - if numerator is 0, denominator is 1, and exponent is 0.8, for 50 CPU we will pick 4.3% threshold It must be defined for the CUSTOM_ADAPTIVE strategy.
Numerator float64
The numerator affects vertical stretch of function used in adaptive threshold - smaller number will create smaller threshold.It must be defined for the CUSTOM_ADAPTIVE strategy.
Percentage float64
Percentage of a how much difference should there be between the current pod requests and the new recommendation. It must be defined for the PERCENTAGE strategy.
type This property is required. String
Defines apply theshold strategy type. - PERCENTAGE - recommendation will be applied when diff of current requests and new recommendation is greater than set value - DEFAULT_ADAPTIVE - will pick larger threshold percentage for small workloads and smaller percentage for large workloads. - CUSTOM_ADAPTIVE - works in same way as DEFAULT_ADAPTIVE, but it allows to tweak parameters of adaptive threshold formula: percentage = numerator/(currentRequest + denominator)^exponent. This strategy is for advance use cases, we recommend to use DEFAULT_ADAPTIVE strategy.
denominator String
If denominator is close or equal to 0, the threshold will be much bigger for small values.For example when numerator, exponent is 1 and denominator is 0 the threshold for 0.5 req. CPU will be 200%.It must be defined for the CUSTOM_ADAPTIVE strategy.
exponent Double
The exponent changes how fast the curve is going down. The smaller value will cause that we won’t pick extremely small number for big resources, for example: - if numerator is 0, denominator is 1, and exponent is 1, for 50 CPU we will pick 2% threshold - if numerator is 0, denominator is 1, and exponent is 0.8, for 50 CPU we will pick 4.3% threshold It must be defined for the CUSTOM_ADAPTIVE strategy.
numerator Double
The numerator affects vertical stretch of function used in adaptive threshold - smaller number will create smaller threshold.It must be defined for the CUSTOM_ADAPTIVE strategy.
percentage Double
Percentage of a how much difference should there be between the current pod requests and the new recommendation. It must be defined for the PERCENTAGE strategy.
type This property is required. string
Defines apply theshold strategy type. - PERCENTAGE - recommendation will be applied when diff of current requests and new recommendation is greater than set value - DEFAULT_ADAPTIVE - will pick larger threshold percentage for small workloads and smaller percentage for large workloads. - CUSTOM_ADAPTIVE - works in same way as DEFAULT_ADAPTIVE, but it allows to tweak parameters of adaptive threshold formula: percentage = numerator/(currentRequest + denominator)^exponent. This strategy is for advance use cases, we recommend to use DEFAULT_ADAPTIVE strategy.
denominator string
If denominator is close or equal to 0, the threshold will be much bigger for small values.For example when numerator, exponent is 1 and denominator is 0 the threshold for 0.5 req. CPU will be 200%.It must be defined for the CUSTOM_ADAPTIVE strategy.
exponent number
The exponent changes how fast the curve is going down. The smaller value will cause that we won’t pick extremely small number for big resources, for example: - if numerator is 0, denominator is 1, and exponent is 1, for 50 CPU we will pick 2% threshold - if numerator is 0, denominator is 1, and exponent is 0.8, for 50 CPU we will pick 4.3% threshold It must be defined for the CUSTOM_ADAPTIVE strategy.
numerator number
The numerator affects vertical stretch of function used in adaptive threshold - smaller number will create smaller threshold.It must be defined for the CUSTOM_ADAPTIVE strategy.
percentage number
Percentage of a how much difference should there be between the current pod requests and the new recommendation. It must be defined for the PERCENTAGE strategy.
type This property is required. str
Defines apply theshold strategy type. - PERCENTAGE - recommendation will be applied when diff of current requests and new recommendation is greater than set value - DEFAULT_ADAPTIVE - will pick larger threshold percentage for small workloads and smaller percentage for large workloads. - CUSTOM_ADAPTIVE - works in same way as DEFAULT_ADAPTIVE, but it allows to tweak parameters of adaptive threshold formula: percentage = numerator/(currentRequest + denominator)^exponent. This strategy is for advance use cases, we recommend to use DEFAULT_ADAPTIVE strategy.
denominator str
If denominator is close or equal to 0, the threshold will be much bigger for small values.For example when numerator, exponent is 1 and denominator is 0 the threshold for 0.5 req. CPU will be 200%.It must be defined for the CUSTOM_ADAPTIVE strategy.
exponent float
The exponent changes how fast the curve is going down. The smaller value will cause that we won’t pick extremely small number for big resources, for example: - if numerator is 0, denominator is 1, and exponent is 1, for 50 CPU we will pick 2% threshold - if numerator is 0, denominator is 1, and exponent is 0.8, for 50 CPU we will pick 4.3% threshold It must be defined for the CUSTOM_ADAPTIVE strategy.
numerator float
The numerator affects vertical stretch of function used in adaptive threshold - smaller number will create smaller threshold.It must be defined for the CUSTOM_ADAPTIVE strategy.
percentage float
Percentage of a how much difference should there be between the current pod requests and the new recommendation. It must be defined for the PERCENTAGE strategy.
type This property is required. String
Defines apply theshold strategy type. - PERCENTAGE - recommendation will be applied when diff of current requests and new recommendation is greater than set value - DEFAULT_ADAPTIVE - will pick larger threshold percentage for small workloads and smaller percentage for large workloads. - CUSTOM_ADAPTIVE - works in same way as DEFAULT_ADAPTIVE, but it allows to tweak parameters of adaptive threshold formula: percentage = numerator/(currentRequest + denominator)^exponent. This strategy is for advance use cases, we recommend to use DEFAULT_ADAPTIVE strategy.
denominator String
If denominator is close or equal to 0, the threshold will be much bigger for small values.For example when numerator, exponent is 1 and denominator is 0 the threshold for 0.5 req. CPU will be 200%.It must be defined for the CUSTOM_ADAPTIVE strategy.
exponent Number
The exponent changes how fast the curve is going down. The smaller value will cause that we won’t pick extremely small number for big resources, for example: - if numerator is 0, denominator is 1, and exponent is 1, for 50 CPU we will pick 2% threshold - if numerator is 0, denominator is 1, and exponent is 0.8, for 50 CPU we will pick 4.3% threshold It must be defined for the CUSTOM_ADAPTIVE strategy.
numerator Number
The numerator affects vertical stretch of function used in adaptive threshold - smaller number will create smaller threshold.It must be defined for the CUSTOM_ADAPTIVE strategy.
percentage Number
Percentage of a how much difference should there be between the current pod requests and the new recommendation. It must be defined for the PERCENTAGE strategy.

WorkloadScalingPolicyCpuLimit
, WorkloadScalingPolicyCpuLimitArgs

Type This property is required. string
Defines limit strategy type. - NO_LIMIT - removes the resource limit even if it was specified in the workload spec. - MULTIPLIER - used to calculate the resource limit. The final value is determined by multiplying the resource request by the specified factor.
Multiplier double
Multiplier used to calculate the resource limit. It must be defined for the MULTIPLIER strategy.
Type This property is required. string
Defines limit strategy type. - NO_LIMIT - removes the resource limit even if it was specified in the workload spec. - MULTIPLIER - used to calculate the resource limit. The final value is determined by multiplying the resource request by the specified factor.
Multiplier float64
Multiplier used to calculate the resource limit. It must be defined for the MULTIPLIER strategy.
type This property is required. String
Defines limit strategy type. - NO_LIMIT - removes the resource limit even if it was specified in the workload spec. - MULTIPLIER - used to calculate the resource limit. The final value is determined by multiplying the resource request by the specified factor.
multiplier Double
Multiplier used to calculate the resource limit. It must be defined for the MULTIPLIER strategy.
type This property is required. string
Defines limit strategy type. - NO_LIMIT - removes the resource limit even if it was specified in the workload spec. - MULTIPLIER - used to calculate the resource limit. The final value is determined by multiplying the resource request by the specified factor.
multiplier number
Multiplier used to calculate the resource limit. It must be defined for the MULTIPLIER strategy.
type This property is required. str
Defines limit strategy type. - NO_LIMIT - removes the resource limit even if it was specified in the workload spec. - MULTIPLIER - used to calculate the resource limit. The final value is determined by multiplying the resource request by the specified factor.
multiplier float
Multiplier used to calculate the resource limit. It must be defined for the MULTIPLIER strategy.
type This property is required. String
Defines limit strategy type. - NO_LIMIT - removes the resource limit even if it was specified in the workload spec. - MULTIPLIER - used to calculate the resource limit. The final value is determined by multiplying the resource request by the specified factor.
multiplier Number
Multiplier used to calculate the resource limit. It must be defined for the MULTIPLIER strategy.

WorkloadScalingPolicyDownscaling
, WorkloadScalingPolicyDownscalingArgs

ApplyType string
Defines the apply type to be used when downscaling. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED - pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
ApplyType string
Defines the apply type to be used when downscaling. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED - pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
applyType String
Defines the apply type to be used when downscaling. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED - pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
applyType string
Defines the apply type to be used when downscaling. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED - pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
apply_type str
Defines the apply type to be used when downscaling. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED - pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
applyType String
Defines the apply type to be used when downscaling. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED - pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)

WorkloadScalingPolicyMemory
, WorkloadScalingPolicyMemoryArgs

ApplyThreshold double
The threshold of when to apply the recommendation. Recommendation will be applied when diff of current requests and new recommendation is greater than set value

Deprecated: Deprecated

ApplyThresholdStrategy WorkloadScalingPolicyMemoryApplyThresholdStrategy
Resource apply threshold strategy settings. The default strategy is PERCENTAGE with percentage value set to 0.1.
Args List<string>
The arguments for the function - i.e. for QUANTILE this should be a [0, 1] float. MAX doesn't accept any args
Function string
The function used to calculate the resource recommendation. Supported values: QUANTILE, MAX
Limit WorkloadScalingPolicyMemoryLimit
Resource limit settings
LookBackPeriodSeconds double
The look back period in seconds for the recommendation.
ManagementOption string
Disables management for a single resource when set to READ_ONLY. The resource will use its original workload template requests and limits. Supported value: READ_ONLY. Minimum required workload-autoscaler version: v0.23.1.
Max double
Max values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
Min double
Min values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
Overhead double
Overhead for the recommendation, e.g. 0.1 will result in 10% higher recommendation
ApplyThreshold float64
The threshold of when to apply the recommendation. Recommendation will be applied when diff of current requests and new recommendation is greater than set value

Deprecated: Deprecated

ApplyThresholdStrategy WorkloadScalingPolicyMemoryApplyThresholdStrategy
Resource apply threshold strategy settings. The default strategy is PERCENTAGE with percentage value set to 0.1.
Args []string
The arguments for the function - i.e. for QUANTILE this should be a [0, 1] float. MAX doesn't accept any args
Function string
The function used to calculate the resource recommendation. Supported values: QUANTILE, MAX
Limit WorkloadScalingPolicyMemoryLimit
Resource limit settings
LookBackPeriodSeconds float64
The look back period in seconds for the recommendation.
ManagementOption string
Disables management for a single resource when set to READ_ONLY. The resource will use its original workload template requests and limits. Supported value: READ_ONLY. Minimum required workload-autoscaler version: v0.23.1.
Max float64
Max values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
Min float64
Min values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
Overhead float64
Overhead for the recommendation, e.g. 0.1 will result in 10% higher recommendation
applyThreshold Double
The threshold of when to apply the recommendation. Recommendation will be applied when diff of current requests and new recommendation is greater than set value

Deprecated: Deprecated

applyThresholdStrategy WorkloadScalingPolicyMemoryApplyThresholdStrategy
Resource apply threshold strategy settings. The default strategy is PERCENTAGE with percentage value set to 0.1.
args List<String>
The arguments for the function - i.e. for QUANTILE this should be a [0, 1] float. MAX doesn't accept any args
function String
The function used to calculate the resource recommendation. Supported values: QUANTILE, MAX
limit WorkloadScalingPolicyMemoryLimit
Resource limit settings
lookBackPeriodSeconds Double
The look back period in seconds for the recommendation.
managementOption String
Disables management for a single resource when set to READ_ONLY. The resource will use its original workload template requests and limits. Supported value: READ_ONLY. Minimum required workload-autoscaler version: v0.23.1.
max Double
Max values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
min Double
Min values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
overhead Double
Overhead for the recommendation, e.g. 0.1 will result in 10% higher recommendation
applyThreshold number
The threshold of when to apply the recommendation. Recommendation will be applied when diff of current requests and new recommendation is greater than set value

Deprecated: Deprecated

applyThresholdStrategy WorkloadScalingPolicyMemoryApplyThresholdStrategy
Resource apply threshold strategy settings. The default strategy is PERCENTAGE with percentage value set to 0.1.
args string[]
The arguments for the function - i.e. for QUANTILE this should be a [0, 1] float. MAX doesn't accept any args
function string
The function used to calculate the resource recommendation. Supported values: QUANTILE, MAX
limit WorkloadScalingPolicyMemoryLimit
Resource limit settings
lookBackPeriodSeconds number
The look back period in seconds for the recommendation.
managementOption string
Disables management for a single resource when set to READ_ONLY. The resource will use its original workload template requests and limits. Supported value: READ_ONLY. Minimum required workload-autoscaler version: v0.23.1.
max number
Max values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
min number
Min values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
overhead number
Overhead for the recommendation, e.g. 0.1 will result in 10% higher recommendation
apply_threshold float
The threshold of when to apply the recommendation. Recommendation will be applied when diff of current requests and new recommendation is greater than set value

Deprecated: Deprecated

apply_threshold_strategy WorkloadScalingPolicyMemoryApplyThresholdStrategy
Resource apply threshold strategy settings. The default strategy is PERCENTAGE with percentage value set to 0.1.
args Sequence[str]
The arguments for the function - i.e. for QUANTILE this should be a [0, 1] float. MAX doesn't accept any args
function str
The function used to calculate the resource recommendation. Supported values: QUANTILE, MAX
limit WorkloadScalingPolicyMemoryLimit
Resource limit settings
look_back_period_seconds float
The look back period in seconds for the recommendation.
management_option str
Disables management for a single resource when set to READ_ONLY. The resource will use its original workload template requests and limits. Supported value: READ_ONLY. Minimum required workload-autoscaler version: v0.23.1.
max float
Max values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
min float
Min values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
overhead float
Overhead for the recommendation, e.g. 0.1 will result in 10% higher recommendation
applyThreshold Number
The threshold of when to apply the recommendation. Recommendation will be applied when diff of current requests and new recommendation is greater than set value

Deprecated: Deprecated

applyThresholdStrategy Property Map
Resource apply threshold strategy settings. The default strategy is PERCENTAGE with percentage value set to 0.1.
args List<String>
The arguments for the function - i.e. for QUANTILE this should be a [0, 1] float. MAX doesn't accept any args
function String
The function used to calculate the resource recommendation. Supported values: QUANTILE, MAX
limit Property Map
Resource limit settings
lookBackPeriodSeconds Number
The look back period in seconds for the recommendation.
managementOption String
Disables management for a single resource when set to READ_ONLY. The resource will use its original workload template requests and limits. Supported value: READ_ONLY. Minimum required workload-autoscaler version: v0.23.1.
max Number
Max values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
min Number
Min values for the recommendation, applies to every container. For memory - this is in MiB, for CPU - this is in cores.
overhead Number
Overhead for the recommendation, e.g. 0.1 will result in 10% higher recommendation

WorkloadScalingPolicyMemoryApplyThresholdStrategy
, WorkloadScalingPolicyMemoryApplyThresholdStrategyArgs

Type This property is required. string
Defines apply theshold strategy type. - PERCENTAGE - recommendation will be applied when diff of current requests and new recommendation is greater than set value - DEFAULT_ADAPTIVE - will pick larger threshold percentage for small workloads and smaller percentage for large workloads. - CUSTOM_ADAPTIVE - works in same way as DEFAULT_ADAPTIVE, but it allows to tweak parameters of adaptive threshold formula: percentage = numerator/(currentRequest + denominator)^exponent. This strategy is for advance use cases, we recommend to use DEFAULT_ADAPTIVE strategy.
Denominator string
If denominator is close or equal to 0, the threshold will be much bigger for small values.For example when numerator, exponent is 1 and denominator is 0 the threshold for 0.5 req. CPU will be 200%.It must be defined for the CUSTOM_ADAPTIVE strategy.
Exponent double
The exponent changes how fast the curve is going down. The smaller value will cause that we won’t pick extremely small number for big resources, for example: - if numerator is 0, denominator is 1, and exponent is 1, for 50 CPU we will pick 2% threshold - if numerator is 0, denominator is 1, and exponent is 0.8, for 50 CPU we will pick 4.3% threshold It must be defined for the CUSTOM_ADAPTIVE strategy.
Numerator double
The numerator affects vertical stretch of function used in adaptive threshold - smaller number will create smaller threshold.It must be defined for the CUSTOM_ADAPTIVE strategy.
Percentage double
Percentage of a how much difference should there be between the current pod requests and the new recommendation. It must be defined for the PERCENTAGE strategy.
Type This property is required. string
Defines apply theshold strategy type. - PERCENTAGE - recommendation will be applied when diff of current requests and new recommendation is greater than set value - DEFAULT_ADAPTIVE - will pick larger threshold percentage for small workloads and smaller percentage for large workloads. - CUSTOM_ADAPTIVE - works in same way as DEFAULT_ADAPTIVE, but it allows to tweak parameters of adaptive threshold formula: percentage = numerator/(currentRequest + denominator)^exponent. This strategy is for advance use cases, we recommend to use DEFAULT_ADAPTIVE strategy.
Denominator string
If denominator is close or equal to 0, the threshold will be much bigger for small values.For example when numerator, exponent is 1 and denominator is 0 the threshold for 0.5 req. CPU will be 200%.It must be defined for the CUSTOM_ADAPTIVE strategy.
Exponent float64
The exponent changes how fast the curve is going down. The smaller value will cause that we won’t pick extremely small number for big resources, for example: - if numerator is 0, denominator is 1, and exponent is 1, for 50 CPU we will pick 2% threshold - if numerator is 0, denominator is 1, and exponent is 0.8, for 50 CPU we will pick 4.3% threshold It must be defined for the CUSTOM_ADAPTIVE strategy.
Numerator float64
The numerator affects vertical stretch of function used in adaptive threshold - smaller number will create smaller threshold.It must be defined for the CUSTOM_ADAPTIVE strategy.
Percentage float64
Percentage of a how much difference should there be between the current pod requests and the new recommendation. It must be defined for the PERCENTAGE strategy.
type This property is required. String
Defines apply theshold strategy type. - PERCENTAGE - recommendation will be applied when diff of current requests and new recommendation is greater than set value - DEFAULT_ADAPTIVE - will pick larger threshold percentage for small workloads and smaller percentage for large workloads. - CUSTOM_ADAPTIVE - works in same way as DEFAULT_ADAPTIVE, but it allows to tweak parameters of adaptive threshold formula: percentage = numerator/(currentRequest + denominator)^exponent. This strategy is for advance use cases, we recommend to use DEFAULT_ADAPTIVE strategy.
denominator String
If denominator is close or equal to 0, the threshold will be much bigger for small values.For example when numerator, exponent is 1 and denominator is 0 the threshold for 0.5 req. CPU will be 200%.It must be defined for the CUSTOM_ADAPTIVE strategy.
exponent Double
The exponent changes how fast the curve is going down. The smaller value will cause that we won’t pick extremely small number for big resources, for example: - if numerator is 0, denominator is 1, and exponent is 1, for 50 CPU we will pick 2% threshold - if numerator is 0, denominator is 1, and exponent is 0.8, for 50 CPU we will pick 4.3% threshold It must be defined for the CUSTOM_ADAPTIVE strategy.
numerator Double
The numerator affects vertical stretch of function used in adaptive threshold - smaller number will create smaller threshold.It must be defined for the CUSTOM_ADAPTIVE strategy.
percentage Double
Percentage of a how much difference should there be between the current pod requests and the new recommendation. It must be defined for the PERCENTAGE strategy.
type This property is required. string
Defines apply theshold strategy type. - PERCENTAGE - recommendation will be applied when diff of current requests and new recommendation is greater than set value - DEFAULT_ADAPTIVE - will pick larger threshold percentage for small workloads and smaller percentage for large workloads. - CUSTOM_ADAPTIVE - works in same way as DEFAULT_ADAPTIVE, but it allows to tweak parameters of adaptive threshold formula: percentage = numerator/(currentRequest + denominator)^exponent. This strategy is for advance use cases, we recommend to use DEFAULT_ADAPTIVE strategy.
denominator string
If denominator is close or equal to 0, the threshold will be much bigger for small values.For example when numerator, exponent is 1 and denominator is 0 the threshold for 0.5 req. CPU will be 200%.It must be defined for the CUSTOM_ADAPTIVE strategy.
exponent number
The exponent changes how fast the curve is going down. The smaller value will cause that we won’t pick extremely small number for big resources, for example: - if numerator is 0, denominator is 1, and exponent is 1, for 50 CPU we will pick 2% threshold - if numerator is 0, denominator is 1, and exponent is 0.8, for 50 CPU we will pick 4.3% threshold It must be defined for the CUSTOM_ADAPTIVE strategy.
numerator number
The numerator affects vertical stretch of function used in adaptive threshold - smaller number will create smaller threshold.It must be defined for the CUSTOM_ADAPTIVE strategy.
percentage number
Percentage of a how much difference should there be between the current pod requests and the new recommendation. It must be defined for the PERCENTAGE strategy.
type This property is required. str
Defines apply theshold strategy type. - PERCENTAGE - recommendation will be applied when diff of current requests and new recommendation is greater than set value - DEFAULT_ADAPTIVE - will pick larger threshold percentage for small workloads and smaller percentage for large workloads. - CUSTOM_ADAPTIVE - works in same way as DEFAULT_ADAPTIVE, but it allows to tweak parameters of adaptive threshold formula: percentage = numerator/(currentRequest + denominator)^exponent. This strategy is for advance use cases, we recommend to use DEFAULT_ADAPTIVE strategy.
denominator str
If denominator is close or equal to 0, the threshold will be much bigger for small values.For example when numerator, exponent is 1 and denominator is 0 the threshold for 0.5 req. CPU will be 200%.It must be defined for the CUSTOM_ADAPTIVE strategy.
exponent float
The exponent changes how fast the curve is going down. The smaller value will cause that we won’t pick extremely small number for big resources, for example: - if numerator is 0, denominator is 1, and exponent is 1, for 50 CPU we will pick 2% threshold - if numerator is 0, denominator is 1, and exponent is 0.8, for 50 CPU we will pick 4.3% threshold It must be defined for the CUSTOM_ADAPTIVE strategy.
numerator float
The numerator affects vertical stretch of function used in adaptive threshold - smaller number will create smaller threshold.It must be defined for the CUSTOM_ADAPTIVE strategy.
percentage float
Percentage of a how much difference should there be between the current pod requests and the new recommendation. It must be defined for the PERCENTAGE strategy.
type This property is required. String
Defines apply theshold strategy type. - PERCENTAGE - recommendation will be applied when diff of current requests and new recommendation is greater than set value - DEFAULT_ADAPTIVE - will pick larger threshold percentage for small workloads and smaller percentage for large workloads. - CUSTOM_ADAPTIVE - works in same way as DEFAULT_ADAPTIVE, but it allows to tweak parameters of adaptive threshold formula: percentage = numerator/(currentRequest + denominator)^exponent. This strategy is for advance use cases, we recommend to use DEFAULT_ADAPTIVE strategy.
denominator String
If denominator is close or equal to 0, the threshold will be much bigger for small values.For example when numerator, exponent is 1 and denominator is 0 the threshold for 0.5 req. CPU will be 200%.It must be defined for the CUSTOM_ADAPTIVE strategy.
exponent Number
The exponent changes how fast the curve is going down. The smaller value will cause that we won’t pick extremely small number for big resources, for example: - if numerator is 0, denominator is 1, and exponent is 1, for 50 CPU we will pick 2% threshold - if numerator is 0, denominator is 1, and exponent is 0.8, for 50 CPU we will pick 4.3% threshold It must be defined for the CUSTOM_ADAPTIVE strategy.
numerator Number
The numerator affects vertical stretch of function used in adaptive threshold - smaller number will create smaller threshold.It must be defined for the CUSTOM_ADAPTIVE strategy.
percentage Number
Percentage of a how much difference should there be between the current pod requests and the new recommendation. It must be defined for the PERCENTAGE strategy.

WorkloadScalingPolicyMemoryEvent
, WorkloadScalingPolicyMemoryEventArgs

ApplyType string
Defines the apply type to be used when applying recommendation for memory related event. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED - pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
ApplyType string
Defines the apply type to be used when applying recommendation for memory related event. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED - pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
applyType String
Defines the apply type to be used when applying recommendation for memory related event. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED - pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
applyType string
Defines the apply type to be used when applying recommendation for memory related event. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED - pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
apply_type str
Defines the apply type to be used when applying recommendation for memory related event. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED - pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)
applyType String
Defines the apply type to be used when applying recommendation for memory related event. - IMMEDIATE - pods are restarted immediately when new recommendation is generated. - DEFERRED - pods are not restarted and recommendation values are applied during natural restarts only (new deployment, etc.)

WorkloadScalingPolicyMemoryLimit
, WorkloadScalingPolicyMemoryLimitArgs

Type This property is required. string
Defines limit strategy type. - NO_LIMIT - removes the resource limit even if it was specified in the workload spec. - MULTIPLIER - used to calculate the resource limit. The final value is determined by multiplying the resource request by the specified factor.
Multiplier double
Multiplier used to calculate the resource limit. It must be defined for the MULTIPLIER strategy.
Type This property is required. string
Defines limit strategy type. - NO_LIMIT - removes the resource limit even if it was specified in the workload spec. - MULTIPLIER - used to calculate the resource limit. The final value is determined by multiplying the resource request by the specified factor.
Multiplier float64
Multiplier used to calculate the resource limit. It must be defined for the MULTIPLIER strategy.
type This property is required. String
Defines limit strategy type. - NO_LIMIT - removes the resource limit even if it was specified in the workload spec. - MULTIPLIER - used to calculate the resource limit. The final value is determined by multiplying the resource request by the specified factor.
multiplier Double
Multiplier used to calculate the resource limit. It must be defined for the MULTIPLIER strategy.
type This property is required. string
Defines limit strategy type. - NO_LIMIT - removes the resource limit even if it was specified in the workload spec. - MULTIPLIER - used to calculate the resource limit. The final value is determined by multiplying the resource request by the specified factor.
multiplier number
Multiplier used to calculate the resource limit. It must be defined for the MULTIPLIER strategy.
type This property is required. str
Defines limit strategy type. - NO_LIMIT - removes the resource limit even if it was specified in the workload spec. - MULTIPLIER - used to calculate the resource limit. The final value is determined by multiplying the resource request by the specified factor.
multiplier float
Multiplier used to calculate the resource limit. It must be defined for the MULTIPLIER strategy.
type This property is required. String
Defines limit strategy type. - NO_LIMIT - removes the resource limit even if it was specified in the workload spec. - MULTIPLIER - used to calculate the resource limit. The final value is determined by multiplying the resource request by the specified factor.
multiplier Number
Multiplier used to calculate the resource limit. It must be defined for the MULTIPLIER strategy.

WorkloadScalingPolicyStartup
, WorkloadScalingPolicyStartupArgs

PeriodSeconds double
Defines the duration (in seconds) during which elevated resource usage is expected at startup. When set, recommendations will be adjusted to disregard resource spikes within this period. If not specified, the workload will receive standard recommendations without startup considerations.
PeriodSeconds float64
Defines the duration (in seconds) during which elevated resource usage is expected at startup. When set, recommendations will be adjusted to disregard resource spikes within this period. If not specified, the workload will receive standard recommendations without startup considerations.
periodSeconds Double
Defines the duration (in seconds) during which elevated resource usage is expected at startup. When set, recommendations will be adjusted to disregard resource spikes within this period. If not specified, the workload will receive standard recommendations without startup considerations.
periodSeconds number
Defines the duration (in seconds) during which elevated resource usage is expected at startup. When set, recommendations will be adjusted to disregard resource spikes within this period. If not specified, the workload will receive standard recommendations without startup considerations.
period_seconds float
Defines the duration (in seconds) during which elevated resource usage is expected at startup. When set, recommendations will be adjusted to disregard resource spikes within this period. If not specified, the workload will receive standard recommendations without startup considerations.
periodSeconds Number
Defines the duration (in seconds) during which elevated resource usage is expected at startup. When set, recommendations will be adjusted to disregard resource spikes within this period. If not specified, the workload will receive standard recommendations without startup considerations.

WorkloadScalingPolicyTimeouts
, WorkloadScalingPolicyTimeoutsArgs

Create string
Delete string
Read string
Update string
Create string
Delete string
Read string
Update string
create String
delete String
read String
update String
create string
delete string
read string
update string
create str
delete str
read str
update str
create String
delete String
read String
update String

Package Details

Repository
castai castai/terraform-provider-castai
License
Notes
This Pulumi package is based on the castai Terraform Provider.