Grafana v0.0.10, May 21 23

grafana.MachineLearningOutlierDetector

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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

new MachineLearningOutlierDetector(name: string, args: MachineLearningOutlierDetectorArgs, opts?: CustomResourceOptions);

@overload
def MachineLearningOutlierDetector(resource_name: 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)
@overload
def MachineLearningOutlierDetector(resource_name: str,
                                   args: MachineLearningOutlierDetectorArgs,
                                   opts: Optional[ResourceOptions] = 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.

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.

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 Lbrlabs.PulumiPackage.Grafana.Inputs.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 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. Defaults to 300.
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. Defaults to 300.
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,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. Defaults to 300.
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 {[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. Defaults to 300.
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. 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.
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. Defaults to 300.
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 Lbrlabs.PulumiPackage.Grafana.Inputs.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. 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, 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. 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]interface{}: 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 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. 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,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 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. 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]: 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. 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, 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. Defaults to 300.
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

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 Lbrlabs.PulumiPackage.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