Docs Home Pulumi IaC Pulumi ESC Pulumi Cloud Packages Tutorials
  1. Packages
  2. Grafana Cloud
  3. API Docs
  4. MachineLearningOutlierDetector
Grafana v0.5.1 published on Wednesday, Jun 12, 2024 by pulumiverse
pulumiverse/pulumi-grafana

grafana.MachineLearningOutlierDetector

Explore with Pulumi AI

grafana logo
Grafana v0.5.1 published on Wednesday, Jun 12, 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 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.

    Constructor 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" },
    },
    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"),
	},
	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"))
    .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_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",
    },
    datasourceUid: "string",
    description: "string",
    interval: 0,
    name: "string",
});

    type: grafana:MachineLearningOutlierDetector
properties:
    algorithm:
        config:
            epsilon: 0
        name: string
        sensitivity: 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.

    Deprecated: Use datasource_uid instead.

    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.
    Name string
    The name of the algorithm to use ('mad' or 'dbscan').
    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.

    Deprecated: Use datasource_uid instead.

    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.
    Name string
    The name of the algorithm to use ('mad' or 'dbscan').
    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.

    Deprecated: Use datasource_uid instead.

    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.
    name String
    The name of the algorithm to use ('mad' or 'dbscan').
    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.

    Deprecated: Use datasource_uid instead.

    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.
    name string
    The name of the algorithm to use ('mad' or 'dbscan').
    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.

    Deprecated: Use datasource_uid instead.

    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.
    name str
    The name of the algorithm to use ('mad' or 'dbscan').
    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.

    Deprecated: Use datasource_uid instead.

    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.
    name String
    The name of the algorithm to use ('mad' or 'dbscan').

    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.

    Deprecated: Use datasource_uid instead.

    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 algorithm to use ('mad' or 'dbscan').
    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.

    Deprecated: Use datasource_uid instead.

    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 algorithm to use ('mad' or 'dbscan').
    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.

    Deprecated: Use datasource_uid instead.

    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 algorithm to use ('mad' or 'dbscan').
    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.

    Deprecated: Use datasource_uid instead.

    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 algorithm to use ('mad' or 'dbscan').
    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.

    Deprecated: Use datasource_uid instead.

    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 algorithm to use ('mad' or 'dbscan').
    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.

    Deprecated: Use datasource_uid instead.

    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 algorithm to use ('mad' or 'dbscan').
    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)

    Import

    $ pulumi import grafana:index/machineLearningOutlierDetector:MachineLearningOutlierDetector 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 v0.5.1 published on Wednesday, Jun 12, 2024 by pulumiverse
    pulumiverse/pulumi-grafana