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Grafana v0.4.2 published on Monday, Apr 15, 2024 by pulumiverse
pulumiverse/pulumi-grafana

grafana.MachineLearningOutlierDetector

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Grafana v0.4.2 published on Monday, Apr 15, 2024 by pulumiverse
pulumiverse/pulumi-grafana

    Create MachineLearningOutlierDetector Resource

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

    Constructor syntax

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

@overload
def MachineLearningOutlierDetector(resource_name: str,
                                   opts: Optional[ResourceOptions] = None,
                                   algorithm: Optional[MachineLearningOutlierDetectorAlgorithmArgs] = None,
                                   datasource_type: Optional[str] = None,
                                   metric: Optional[str] = None,
                                   query_params: Optional[Mapping[str, Any]] = None,
                                   datasource_id: Optional[int] = None,
                                   datasource_uid: Optional[str] = None,
                                   description: Optional[str] = None,
                                   interval: Optional[int] = None,
                                   name: Optional[str] = None)
    func NewMachineLearningOutlierDetector(ctx *Context, name string, args MachineLearningOutlierDetectorArgs, opts ...ResourceOption) (*MachineLearningOutlierDetector, error)
    public MachineLearningOutlierDetector(string name, MachineLearningOutlierDetectorArgs args, CustomResourceOptions? opts = null)
    public MachineLearningOutlierDetector(String name, MachineLearningOutlierDetectorArgs args)
public MachineLearningOutlierDetector(String name, MachineLearningOutlierDetectorArgs args, CustomResourceOptions options)

    type: grafana:MachineLearningOutlierDetector
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 MachineLearningOutlierDetectorArgs
    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 MachineLearningOutlierDetectorArgs
    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 MachineLearningOutlierDetectorArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args MachineLearningOutlierDetectorArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args MachineLearningOutlierDetectorArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    Example

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

    var machineLearningOutlierDetectorResource = new Grafana.MachineLearningOutlierDetector("machineLearningOutlierDetectorResource", new()
{
    Algorithm = new Grafana.Inputs.MachineLearningOutlierDetectorAlgorithmArgs
    {
        Name = "string",
        Sensitivity = 0,
        Config = new Grafana.Inputs.MachineLearningOutlierDetectorAlgorithmConfigArgs
        {
            Epsilon = 0,
        },
    },
    DatasourceType = "string",
    Metric = "string",
    QueryParams = 
    {
        { "string", "any" },
    },
    DatasourceId = 0,
    DatasourceUid = "string",
    Description = "string",
    Interval = 0,
    Name = "string",
});

    example, err := grafana.NewMachineLearningOutlierDetector(ctx, "machineLearningOutlierDetectorResource", &grafana.MachineLearningOutlierDetectorArgs{
	Algorithm: &grafana.MachineLearningOutlierDetectorAlgorithmArgs{
		Name:        pulumi.String("string"),
		Sensitivity: pulumi.Float64(0),
		Config: &grafana.MachineLearningOutlierDetectorAlgorithmConfigArgs{
			Epsilon: pulumi.Float64(0),
		},
	},
	DatasourceType: pulumi.String("string"),
	Metric:         pulumi.String("string"),
	QueryParams: pulumi.Map{
		"string": pulumi.Any("any"),
	},
	DatasourceId:  pulumi.Int(0),
	DatasourceUid: pulumi.String("string"),
	Description:   pulumi.String("string"),
	Interval:      pulumi.Int(0),
	Name:          pulumi.String("string"),
})

    var machineLearningOutlierDetectorResource = new MachineLearningOutlierDetector("machineLearningOutlierDetectorResource", MachineLearningOutlierDetectorArgs.builder()        
    .algorithm(MachineLearningOutlierDetectorAlgorithmArgs.builder()
        .name("string")
        .sensitivity(0)
        .config(MachineLearningOutlierDetectorAlgorithmConfigArgs.builder()
            .epsilon(0)
            .build())
        .build())
    .datasourceType("string")
    .metric("string")
    .queryParams(Map.of("string", "any"))
    .datasourceId(0)
    .datasourceUid("string")
    .description("string")
    .interval(0)
    .name("string")
    .build());

    machine_learning_outlier_detector_resource = grafana.MachineLearningOutlierDetector("machineLearningOutlierDetectorResource",
    algorithm=grafana.MachineLearningOutlierDetectorAlgorithmArgs(
        name="string",
        sensitivity=0,
        config=grafana.MachineLearningOutlierDetectorAlgorithmConfigArgs(
            epsilon=0,
        ),
    ),
    datasource_type="string",
    metric="string",
    query_params={
        "string": "any",
    },
    datasource_id=0,
    datasource_uid="string",
    description="string",
    interval=0,
    name="string")

    const machineLearningOutlierDetectorResource = new grafana.MachineLearningOutlierDetector("machineLearningOutlierDetectorResource", {
    algorithm: {
        name: "string",
        sensitivity: 0,
        config: {
            epsilon: 0,
        },
    },
    datasourceType: "string",
    metric: "string",
    queryParams: {
        string: "any",
    },
    datasourceId: 0,
    datasourceUid: "string",
    description: "string",
    interval: 0,
    name: "string",
});

    type: grafana:MachineLearningOutlierDetector
properties:
    algorithm:
        config:
            epsilon: 0
        name: string
        sensitivity: 0
    datasourceId: 0
    datasourceType: string
    datasourceUid: string
    description: string
    interval: 0
    metric: string
    name: string
    queryParams:
        string: any

    MachineLearningOutlierDetector Resource Properties

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

    Inputs

    The MachineLearningOutlierDetector resource accepts the following input properties:

    Algorithm Pulumiverse.Grafana.Inputs.MachineLearningOutlierDetectorAlgorithm
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    DatasourceType string
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    Metric string
    The metric used to query the outlier detector results.
    QueryParams Dictionary<string, object>
    An object representing the query params to query Grafana with.
    DatasourceId int
    The id of the datasource to query.
    DatasourceUid string
    The uid of the datasource to query.
    Description string
    A description of the outlier detector.
    Interval int
    The data interval in seconds to monitor.
    Name string
    The name of the outlier detector.
    Algorithm MachineLearningOutlierDetectorAlgorithmArgs
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    DatasourceType string
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    Metric string
    The metric used to query the outlier detector results.
    QueryParams map[string]interface{}
    An object representing the query params to query Grafana with.
    DatasourceId int
    The id of the datasource to query.
    DatasourceUid string
    The uid of the datasource to query.
    Description string
    A description of the outlier detector.
    Interval int
    The data interval in seconds to monitor.
    Name string
    The name of the outlier detector.
    algorithm MachineLearningOutlierDetectorAlgorithm
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    datasourceType String
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    metric String
    The metric used to query the outlier detector results.
    queryParams Map<String,Object>
    An object representing the query params to query Grafana with.
    datasourceId Integer
    The id of the datasource to query.
    datasourceUid String
    The uid of the datasource to query.
    description String
    A description of the outlier detector.
    interval Integer
    The data interval in seconds to monitor.
    name String
    The name of the outlier detector.
    algorithm MachineLearningOutlierDetectorAlgorithm
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    datasourceType string
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    metric string
    The metric used to query the outlier detector results.
    queryParams {[key: string]: any}
    An object representing the query params to query Grafana with.
    datasourceId number
    The id of the datasource to query.
    datasourceUid string
    The uid of the datasource to query.
    description string
    A description of the outlier detector.
    interval number
    The data interval in seconds to monitor.
    name string
    The name of the outlier detector.
    algorithm MachineLearningOutlierDetectorAlgorithmArgs
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    datasource_type str
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    metric str
    The metric used to query the outlier detector results.
    query_params Mapping[str, Any]
    An object representing the query params to query Grafana with.
    datasource_id int
    The id of the datasource to query.
    datasource_uid str
    The uid of the datasource to query.
    description str
    A description of the outlier detector.
    interval int
    The data interval in seconds to monitor.
    name str
    The name of the outlier detector.
    algorithm Property Map
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    datasourceType String
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    metric String
    The metric used to query the outlier detector results.
    queryParams Map<Any>
    An object representing the query params to query Grafana with.
    datasourceId Number
    The id of the datasource to query.
    datasourceUid String
    The uid of the datasource to query.
    description String
    A description of the outlier detector.
    interval Number
    The data interval in seconds to monitor.
    name String
    The name of the outlier detector.

    Outputs

    All input properties are implicitly available as output properties. Additionally, the MachineLearningOutlierDetector 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 MachineLearningOutlierDetector Resource

    Get an existing MachineLearningOutlierDetector 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?: MachineLearningOutlierDetectorState, opts?: CustomResourceOptions): MachineLearningOutlierDetector
    @staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        algorithm: Optional[MachineLearningOutlierDetectorAlgorithmArgs] = None,
        datasource_id: Optional[int] = None,
        datasource_type: Optional[str] = None,
        datasource_uid: Optional[str] = None,
        description: Optional[str] = None,
        interval: Optional[int] = None,
        metric: Optional[str] = None,
        name: Optional[str] = None,
        query_params: Optional[Mapping[str, Any]] = None) -> MachineLearningOutlierDetector
    func GetMachineLearningOutlierDetector(ctx *Context, name string, id IDInput, state *MachineLearningOutlierDetectorState, opts ...ResourceOption) (*MachineLearningOutlierDetector, error)
    public static MachineLearningOutlierDetector Get(string name, Input<string> id, MachineLearningOutlierDetectorState? state, CustomResourceOptions? opts = null)
    public static MachineLearningOutlierDetector get(String name, Output<String> id, MachineLearningOutlierDetectorState state, CustomResourceOptions options)
    Resource lookup is not supported in YAML
    name
    The unique name of the resulting resource.
    id
    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
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    name
    The unique name of the resulting resource.
    id
    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
    The unique name of the resulting resource.
    id
    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
    The unique name of the resulting resource.
    id
    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:
    Algorithm Pulumiverse.Grafana.Inputs.MachineLearningOutlierDetectorAlgorithm
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    DatasourceId int
    The id of the datasource to query.
    DatasourceType string
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    DatasourceUid string
    The uid of the datasource to query.
    Description string
    A description of the outlier detector.
    Interval int
    The data interval in seconds to monitor.
    Metric string
    The metric used to query the outlier detector results.
    Name string
    The name of the outlier detector.
    QueryParams Dictionary<string, object>
    An object representing the query params to query Grafana with.
    Algorithm MachineLearningOutlierDetectorAlgorithmArgs
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    DatasourceId int
    The id of the datasource to query.
    DatasourceType string
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    DatasourceUid string
    The uid of the datasource to query.
    Description string
    A description of the outlier detector.
    Interval int
    The data interval in seconds to monitor.
    Metric string
    The metric used to query the outlier detector results.
    Name string
    The name of the outlier detector.
    QueryParams map[string]interface{}
    An object representing the query params to query Grafana with.
    algorithm MachineLearningOutlierDetectorAlgorithm
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    datasourceId Integer
    The id of the datasource to query.
    datasourceType String
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    datasourceUid String
    The uid of the datasource to query.
    description String
    A description of the outlier detector.
    interval Integer
    The data interval in seconds to monitor.
    metric String
    The metric used to query the outlier detector results.
    name String
    The name of the outlier detector.
    queryParams Map<String,Object>
    An object representing the query params to query Grafana with.
    algorithm MachineLearningOutlierDetectorAlgorithm
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    datasourceId number
    The id of the datasource to query.
    datasourceType string
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    datasourceUid string
    The uid of the datasource to query.
    description string
    A description of the outlier detector.
    interval number
    The data interval in seconds to monitor.
    metric string
    The metric used to query the outlier detector results.
    name string
    The name of the outlier detector.
    queryParams {[key: string]: any}
    An object representing the query params to query Grafana with.
    algorithm MachineLearningOutlierDetectorAlgorithmArgs
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    datasource_id int
    The id of the datasource to query.
    datasource_type str
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    datasource_uid str
    The uid of the datasource to query.
    description str
    A description of the outlier detector.
    interval int
    The data interval in seconds to monitor.
    metric str
    The metric used to query the outlier detector results.
    name str
    The name of the outlier detector.
    query_params Mapping[str, Any]
    An object representing the query params to query Grafana with.
    algorithm Property Map
    The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
    datasourceId Number
    The id of the datasource to query.
    datasourceType String
    The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
    datasourceUid String
    The uid of the datasource to query.
    description String
    A description of the outlier detector.
    interval Number
    The data interval in seconds to monitor.
    metric String
    The metric used to query the outlier detector results.
    name String
    The name of the outlier detector.
    queryParams Map<Any>
    An object representing the query params to query Grafana with.

    Supporting Types

    MachineLearningOutlierDetectorAlgorithm, MachineLearningOutlierDetectorAlgorithmArgs

    Name string
    The name of the algorithm to use ('mad' or 'dbscan').
    Sensitivity double
    Specify the sensitivity of the detector (in range [0,1]).
    Config Pulumiverse.Grafana.Inputs.MachineLearningOutlierDetectorAlgorithmConfig
    For DBSCAN only, specify the configuration map
    Name string
    The name of the algorithm to use ('mad' or 'dbscan').
    Sensitivity float64
    Specify the sensitivity of the detector (in range [0,1]).
    Config MachineLearningOutlierDetectorAlgorithmConfig
    For DBSCAN only, specify the configuration map
    name String
    The name of the algorithm to use ('mad' or 'dbscan').
    sensitivity Double
    Specify the sensitivity of the detector (in range [0,1]).
    config MachineLearningOutlierDetectorAlgorithmConfig
    For DBSCAN only, specify the configuration map
    name string
    The name of the algorithm to use ('mad' or 'dbscan').
    sensitivity number
    Specify the sensitivity of the detector (in range [0,1]).
    config MachineLearningOutlierDetectorAlgorithmConfig
    For DBSCAN only, specify the configuration map
    name str
    The name of the algorithm to use ('mad' or 'dbscan').
    sensitivity float
    Specify the sensitivity of the detector (in range [0,1]).
    config MachineLearningOutlierDetectorAlgorithmConfig
    For DBSCAN only, specify the configuration map
    name String
    The name of the algorithm to use ('mad' or 'dbscan').
    sensitivity Number
    Specify the sensitivity of the detector (in range [0,1]).
    config Property Map
    For DBSCAN only, specify the configuration map

    MachineLearningOutlierDetectorAlgorithmConfig, MachineLearningOutlierDetectorAlgorithmConfigArgs

    Epsilon double
    Specify the epsilon parameter (positive float)
    Epsilon float64
    Specify the epsilon parameter (positive float)
    epsilon Double
    Specify the epsilon parameter (positive float)
    epsilon number
    Specify the epsilon parameter (positive float)
    epsilon float
    Specify the epsilon parameter (positive float)
    epsilon Number
    Specify the epsilon parameter (positive float)

    Package Details

    Repository
    grafana pulumiverse/pulumi-grafana
    License
    Apache-2.0
    Notes
    This Pulumi package is based on the grafana Terraform Provider.
    grafana logo
    Grafana v0.4.2 published on Monday, Apr 15, 2024 by pulumiverse
    pulumiverse/pulumi-grafana