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Grafana v0.7.0 published on Tuesday, Nov 5, 2024 by pulumiverse
pulumiverse/pulumi-grafana

grafana.machineLearning.OutlierDetector

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Grafana v0.7.0 published on Tuesday, Nov 5, 2024 by pulumiverse
pulumiverse/pulumi-grafana

    An outlier detector monitors the results of a query and reports when its values are outside normal bands.

    The normal band is configured by choice of algorithm, its sensitivity and other configuration.

    Visit https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for more details.

    Create OutlierDetector Resource

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

    Constructor syntax

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

@overload
def OutlierDetector(resource_name: str,
                    opts: Optional[ResourceOptions] = None,
                    algorithm: Optional[_machinelearning.OutlierDetectorAlgorithmArgs] = None,
                    datasource_type: Optional[str] = None,
                    datasource_uid: Optional[str] = None,
                    metric: Optional[str] = None,
                    query_params: Optional[Mapping[str, str]] = None,
                    description: Optional[str] = None,
                    interval: Optional[int] = None,
                    name: Optional[str] = None)
    func NewOutlierDetector(ctx *Context, name string, args OutlierDetectorArgs, opts ...ResourceOption) (*OutlierDetector, error)
    public OutlierDetector(string name, OutlierDetectorArgs args, CustomResourceOptions? opts = null)
    public OutlierDetector(String name, OutlierDetectorArgs args)
public OutlierDetector(String name, OutlierDetectorArgs args, CustomResourceOptions options)

    type: grafana:machineLearning:OutlierDetector
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 OutlierDetectorArgs
    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 OutlierDetectorArgs
    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 OutlierDetectorArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args OutlierDetectorArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args OutlierDetectorArgs
    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 outlierDetectorResource = new Grafana.MachineLearning.OutlierDetector("outlierDetectorResource", new()
{
    Algorithm = new Grafana.MachineLearning.Inputs.OutlierDetectorAlgorithmArgs
    {
        Name = "string",
        Sensitivity = 0,
        Config = new Grafana.MachineLearning.Inputs.OutlierDetectorAlgorithmConfigArgs
        {
            Epsilon = 0,
        },
    },
    DatasourceType = "string",
    DatasourceUid = "string",
    Metric = "string",
    QueryParams = 
    {
        { "string", "string" },
    },
    Description = "string",
    Interval = 0,
    Name = "string",
});

    example, err := machineLearning.NewOutlierDetector(ctx, "outlierDetectorResource", &machineLearning.OutlierDetectorArgs{
	Algorithm: &machinelearning.OutlierDetectorAlgorithmArgs{
		Name:        pulumi.String("string"),
		Sensitivity: pulumi.Float64(0),
		Config: &machinelearning.OutlierDetectorAlgorithmConfigArgs{
			Epsilon: pulumi.Float64(0),
		},
	},
	DatasourceType: pulumi.String("string"),
	DatasourceUid:  pulumi.String("string"),
	Metric:         pulumi.String("string"),
	QueryParams: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	Description: pulumi.String("string"),
	Interval:    pulumi.Int(0),
	Name:        pulumi.String("string"),
})

    var outlierDetectorResource = new OutlierDetector("outlierDetectorResource", OutlierDetectorArgs.builder()
    .algorithm(OutlierDetectorAlgorithmArgs.builder()
        .name("string")
        .sensitivity(0)
        .config(OutlierDetectorAlgorithmConfigArgs.builder()
            .epsilon(0)
            .build())
        .build())
    .datasourceType("string")
    .datasourceUid("string")
    .metric("string")
    .queryParams(Map.of("string", "string"))
    .description("string")
    .interval(0)
    .name("string")
    .build());

    outlier_detector_resource = grafana.machine_learning.OutlierDetector("outlierDetectorResource",
    algorithm={
        "name": "string",
        "sensitivity": 0,
        "config": {
            "epsilon": 0,
        },
    },
    datasource_type="string",
    datasource_uid="string",
    metric="string",
    query_params={
        "string": "string",
    },
    description="string",
    interval=0,
    name="string")

    const outlierDetectorResource = new grafana.machinelearning.OutlierDetector("outlierDetectorResource", {
    algorithm: {
        name: "string",
        sensitivity: 0,
        config: {
            epsilon: 0,
        },
    },
    datasourceType: "string",
    datasourceUid: "string",
    metric: "string",
    queryParams: {
        string: "string",
    },
    description: "string",
    interval: 0,
    name: "string",
});

    type: grafana:machineLearning:OutlierDetector
properties:
    algorithm:
        config:
            epsilon: 0
        name: string
        sensitivity: 0
    datasourceType: string
    datasourceUid: string
    description: string
    interval: 0
    metric: string
    name: string
    queryParams:
        string: string

    OutlierDetector 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 OutlierDetector resource accepts the following input properties:

    Algorithm Pulumiverse.Grafana.MachineLearning.Inputs.OutlierDetectorAlgorithm
    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.
    DatasourceUid string
    The uid of the datasource to query.
    Metric string
    The metric used to query the outlier detector results.
    QueryParams Dictionary<string, string>
    An object representing the query params to query Grafana with.
    Description string
    A description of the outlier detector.
    Interval int
    The data interval in seconds to monitor. Defaults to 300.
    Name string
    The name of the outlier detector.
    Algorithm OutlierDetectorAlgorithmArgs
    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.
    DatasourceUid string
    The uid of the datasource to query.
    Metric string
    The metric used to query the outlier detector results.
    QueryParams map[string]string
    An object representing the query params to query Grafana with.
    Description string
    A description of the outlier detector.
    Interval int
    The data interval in seconds to monitor. Defaults to 300.
    Name string
    The name of the outlier detector.
    algorithm OutlierDetectorAlgorithm
    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.
    datasourceUid String
    The uid of the datasource to query.
    metric String
    The metric used to query the outlier detector results.
    queryParams Map<String,String>
    An object representing the query params to query Grafana with.
    description String
    A description of the outlier detector.
    interval Integer
    The data interval in seconds to monitor. Defaults to 300.
    name String
    The name of the outlier detector.
    algorithm OutlierDetectorAlgorithm
    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.
    datasourceUid string
    The uid of the datasource to query.
    metric string
    The metric used to query the outlier detector results.
    queryParams {[key: string]: string}
    An object representing the query params to query Grafana with.
    description string
    A description of the outlier detector.
    interval number
    The data interval in seconds to monitor. Defaults to 300.
    name string
    The name of the outlier detector.
    algorithm machinelearning.OutlierDetectorAlgorithmArgs
    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.
    datasource_uid str
    The uid of the datasource to query.
    metric str
    The metric used to query the outlier detector results.
    query_params Mapping[str, str]
    An object representing the query params to query Grafana with.
    description str
    A description of the outlier detector.
    interval int
    The data interval in seconds to monitor. Defaults to 300.
    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.
    datasourceUid String
    The uid of the datasource to query.
    metric String
    The metric used to query the outlier detector results.
    queryParams Map<String>
    An object representing the query params to query Grafana with.
    description String
    A description of the outlier detector.
    interval Number
    The data interval in seconds to monitor. Defaults to 300.
    name String
    The name of the outlier detector.

    Outputs

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

    Get an existing OutlierDetector 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?: OutlierDetectorState, opts?: CustomResourceOptions): OutlierDetector
    @staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        algorithm: Optional[_machinelearning.OutlierDetectorAlgorithmArgs] = 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, str]] = None) -> OutlierDetector
    func GetOutlierDetector(ctx *Context, name string, id IDInput, state *OutlierDetectorState, opts ...ResourceOption) (*OutlierDetector, error)
    public static OutlierDetector Get(string name, Input<string> id, OutlierDetectorState? state, CustomResourceOptions? opts = null)
    public static OutlierDetector get(String name, Output<String> id, OutlierDetectorState 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.MachineLearning.Inputs.OutlierDetectorAlgorithm
    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.
    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. Defaults to 300.
    Metric string
    The metric used to query the outlier detector results.
    Name string
    The name of the outlier detector.
    QueryParams Dictionary<string, string>
    An object representing the query params to query Grafana with.
    Algorithm OutlierDetectorAlgorithmArgs
    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.
    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. Defaults to 300.
    Metric string
    The metric used to query the outlier detector results.
    Name string
    The name of the outlier detector.
    QueryParams map[string]string
    An object representing the query params to query Grafana with.
    algorithm OutlierDetectorAlgorithm
    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.
    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. Defaults to 300.
    metric String
    The metric used to query the outlier detector results.
    name String
    The name of the outlier detector.
    queryParams Map<String,String>
    An object representing the query params to query Grafana with.
    algorithm OutlierDetectorAlgorithm
    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.
    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. Defaults to 300.
    metric string
    The metric used to query the outlier detector results.
    name string
    The name of the outlier detector.
    queryParams {[key: string]: string}
    An object representing the query params to query Grafana with.
    algorithm machinelearning.OutlierDetectorAlgorithmArgs
    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.
    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. Defaults to 300.
    metric str
    The metric used to query the outlier detector results.
    name str
    The name of the outlier detector.
    query_params Mapping[str, str]
    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.
    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. Defaults to 300.
    metric String
    The metric used to query the outlier detector results.
    name String
    The name of the outlier detector.
    queryParams Map<String>
    An object representing the query params to query Grafana with.

    Supporting Types

    OutlierDetectorAlgorithm, OutlierDetectorAlgorithmArgs

    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.MachineLearning.Inputs.OutlierDetectorAlgorithmConfig
    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 OutlierDetectorAlgorithmConfig
    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 OutlierDetectorAlgorithmConfig
    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 OutlierDetectorAlgorithmConfig
    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 machinelearning.OutlierDetectorAlgorithmConfig
    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

    OutlierDetectorAlgorithmConfig, OutlierDetectorAlgorithmConfigArgs

    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)

    Import

    $ pulumi import grafana:machineLearning/outlierDetector:OutlierDetector name "{{ id }}"

    To learn more about importing existing cloud resources, see Importing resources.

    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 v0.7.0 published on Tuesday, Nov 5, 2024 by pulumiverse
    pulumiverse/pulumi-grafana