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Grafana v0.16.3 published on Monday, Apr 7, 2025 by pulumiverse
pulumiverse/pulumi-grafana

grafana.machineLearning.Alert

Explore with Pulumi AI

Grafana v0.16.3 published on Monday, Apr 7, 2025 by pulumiverse
pulumiverse/pulumi-grafana

Example Usage

Forecast Alert

This alert uses a forecast.

import * as pulumi from "@pulumi/pulumi";
import * as grafana from "@pulumiverse/grafana";

const testAlertJob = new grafana.machinelearning.Job("test_alert_job", {
    name: "Test Job",
    metric: "tf_test_alert_job",
    datasourceType: "prometheus",
    datasourceUid: "abcd12345",
    queryParams: {
        expr: "grafanacloud_grafana_instance_active_user_count",
    },
});
const testJobAlert = new grafana.machinelearning.Alert("test_job_alert", {
    jobId: testAlertJob.id,
    title: "Test Alert",
    anomalyCondition: "any",
    threshold: ">0.8",
    window: "15m",
    noDataState: "OK",
});
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import pulumi
import pulumiverse_grafana as grafana

test_alert_job = grafana.machine_learning.Job("test_alert_job",
    name="Test Job",
    metric="tf_test_alert_job",
    datasource_type="prometheus",
    datasource_uid="abcd12345",
    query_params={
        "expr": "grafanacloud_grafana_instance_active_user_count",
    })
test_job_alert = grafana.machine_learning.Alert("test_job_alert",
    job_id=test_alert_job.id,
    title="Test Alert",
    anomaly_condition="any",
    threshold=">0.8",
    window="15m",
    no_data_state="OK")
Copy
package main

import (
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
	"github.com/pulumiverse/pulumi-grafana/sdk/go/grafana/machinelearning"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		testAlertJob, err := machinelearning.NewJob(ctx, "test_alert_job", &machinelearning.JobArgs{
			Name:           pulumi.String("Test Job"),
			Metric:         pulumi.String("tf_test_alert_job"),
			DatasourceType: pulumi.String("prometheus"),
			DatasourceUid:  pulumi.String("abcd12345"),
			QueryParams: pulumi.StringMap{
				"expr": pulumi.String("grafanacloud_grafana_instance_active_user_count"),
			},
		})
		if err != nil {
			return err
		}
		_, err = machinelearning.NewAlert(ctx, "test_job_alert", &machinelearning.AlertArgs{
			JobId:            testAlertJob.ID(),
			Title:            pulumi.String("Test Alert"),
			AnomalyCondition: pulumi.String("any"),
			Threshold:        pulumi.String(">0.8"),
			Window:           pulumi.String("15m"),
			NoDataState:      pulumi.String("OK"),
		})
		if err != nil {
			return err
		}
		return nil
	})
}
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using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Grafana = Pulumiverse.Grafana;

return await Deployment.RunAsync(() => 
{
    var testAlertJob = new Grafana.MachineLearning.Job("test_alert_job", new()
    {
        Name = "Test Job",
        Metric = "tf_test_alert_job",
        DatasourceType = "prometheus",
        DatasourceUid = "abcd12345",
        QueryParams = 
        {
            { "expr", "grafanacloud_grafana_instance_active_user_count" },
        },
    });

    var testJobAlert = new Grafana.MachineLearning.Alert("test_job_alert", new()
    {
        JobId = testAlertJob.Id,
        Title = "Test Alert",
        AnomalyCondition = "any",
        Threshold = ">0.8",
        Window = "15m",
        NoDataState = "OK",
    });

});
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package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.grafana.machineLearning.Job;
import com.pulumi.grafana.machineLearning.JobArgs;
import com.pulumi.grafana.machineLearning.Alert;
import com.pulumi.grafana.machineLearning.AlertArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var testAlertJob = new Job("testAlertJob", JobArgs.builder()
            .name("Test Job")
            .metric("tf_test_alert_job")
            .datasourceType("prometheus")
            .datasourceUid("abcd12345")
            .queryParams(Map.of("expr", "grafanacloud_grafana_instance_active_user_count"))
            .build());

        var testJobAlert = new Alert("testJobAlert", AlertArgs.builder()
            .jobId(testAlertJob.id())
            .title("Test Alert")
            .anomalyCondition("any")
            .threshold(">0.8")
            .window("15m")
            .noDataState("OK")
            .build());

    }
}
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resources:
  testAlertJob:
    type: grafana:machineLearning:Job
    name: test_alert_job
    properties:
      name: Test Job
      metric: tf_test_alert_job
      datasourceType: prometheus
      datasourceUid: abcd12345
      queryParams:
        expr: grafanacloud_grafana_instance_active_user_count
  testJobAlert:
    type: grafana:machineLearning:Alert
    name: test_job_alert
    properties:
      jobId: ${testAlertJob.id}
      title: Test Alert
      anomalyCondition: any
      threshold: '>0.8'
      window: 15m
      noDataState: OK
Copy

Outlier Alert

This alert uses an outlier detector.

import * as pulumi from "@pulumi/pulumi";
import * as grafana from "@pulumiverse/grafana";

const testAlertOutlierDetector = new grafana.machinelearning.OutlierDetector("test_alert_outlier_detector", {
    name: "Test Outlier",
    metric: "tf_test_alert_outlier",
    datasourceType: "prometheus",
    datasourceUid: "AbCd12345",
    queryParams: {
        expr: "grafanacloud_grafana_instance_active_user_count",
    },
    interval: 300,
    algorithm: {
        name: "dbscan",
        sensitivity: 0.5,
        config: {
            epsilon: 1,
        },
    },
});
const testOutlierAlert = new grafana.machinelearning.Alert("test_outlier_alert", {
    outlierId: testAlertOutlierDetector.id,
    title: "Test Alert",
    window: "1h",
});
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import pulumi
import pulumiverse_grafana as grafana

test_alert_outlier_detector = grafana.machine_learning.OutlierDetector("test_alert_outlier_detector",
    name="Test Outlier",
    metric="tf_test_alert_outlier",
    datasource_type="prometheus",
    datasource_uid="AbCd12345",
    query_params={
        "expr": "grafanacloud_grafana_instance_active_user_count",
    },
    interval=300,
    algorithm={
        "name": "dbscan",
        "sensitivity": 0.5,
        "config": {
            "epsilon": 1,
        },
    })
test_outlier_alert = grafana.machine_learning.Alert("test_outlier_alert",
    outlier_id=test_alert_outlier_detector.id,
    title="Test Alert",
    window="1h")
Copy
package main

import (
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
	"github.com/pulumiverse/pulumi-grafana/sdk/go/grafana/machinelearning"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		testAlertOutlierDetector, err := machinelearning.NewOutlierDetector(ctx, "test_alert_outlier_detector", &machinelearning.OutlierDetectorArgs{
			Name:           pulumi.String("Test Outlier"),
			Metric:         pulumi.String("tf_test_alert_outlier"),
			DatasourceType: pulumi.String("prometheus"),
			DatasourceUid:  pulumi.String("AbCd12345"),
			QueryParams: pulumi.StringMap{
				"expr": pulumi.String("grafanacloud_grafana_instance_active_user_count"),
			},
			Interval: pulumi.Int(300),
			Algorithm: &machinelearning.OutlierDetectorAlgorithmArgs{
				Name:        pulumi.String("dbscan"),
				Sensitivity: pulumi.Float64(0.5),
				Config: &machinelearning.OutlierDetectorAlgorithmConfigArgs{
					Epsilon: pulumi.Float64(1),
				},
			},
		})
		if err != nil {
			return err
		}
		_, err = machinelearning.NewAlert(ctx, "test_outlier_alert", &machinelearning.AlertArgs{
			OutlierId: testAlertOutlierDetector.ID(),
			Title:     pulumi.String("Test Alert"),
			Window:    pulumi.String("1h"),
		})
		if err != nil {
			return err
		}
		return nil
	})
}
Copy
using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Grafana = Pulumiverse.Grafana;

return await Deployment.RunAsync(() => 
{
    var testAlertOutlierDetector = new Grafana.MachineLearning.OutlierDetector("test_alert_outlier_detector", new()
    {
        Name = "Test Outlier",
        Metric = "tf_test_alert_outlier",
        DatasourceType = "prometheus",
        DatasourceUid = "AbCd12345",
        QueryParams = 
        {
            { "expr", "grafanacloud_grafana_instance_active_user_count" },
        },
        Interval = 300,
        Algorithm = new Grafana.MachineLearning.Inputs.OutlierDetectorAlgorithmArgs
        {
            Name = "dbscan",
            Sensitivity = 0.5,
            Config = new Grafana.MachineLearning.Inputs.OutlierDetectorAlgorithmConfigArgs
            {
                Epsilon = 1,
            },
        },
    });

    var testOutlierAlert = new Grafana.MachineLearning.Alert("test_outlier_alert", new()
    {
        OutlierId = testAlertOutlierDetector.Id,
        Title = "Test Alert",
        Window = "1h",
    });

});
Copy
package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.grafana.machineLearning.OutlierDetector;
import com.pulumi.grafana.machineLearning.OutlierDetectorArgs;
import com.pulumi.grafana.machineLearning.inputs.OutlierDetectorAlgorithmArgs;
import com.pulumi.grafana.machineLearning.inputs.OutlierDetectorAlgorithmConfigArgs;
import com.pulumi.grafana.machineLearning.Alert;
import com.pulumi.grafana.machineLearning.AlertArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var testAlertOutlierDetector = new OutlierDetector("testAlertOutlierDetector", OutlierDetectorArgs.builder()
            .name("Test Outlier")
            .metric("tf_test_alert_outlier")
            .datasourceType("prometheus")
            .datasourceUid("AbCd12345")
            .queryParams(Map.of("expr", "grafanacloud_grafana_instance_active_user_count"))
            .interval(300)
            .algorithm(OutlierDetectorAlgorithmArgs.builder()
                .name("dbscan")
                .sensitivity(0.5)
                .config(OutlierDetectorAlgorithmConfigArgs.builder()
                    .epsilon(1)
                    .build())
                .build())
            .build());

        var testOutlierAlert = new Alert("testOutlierAlert", AlertArgs.builder()
            .outlierId(testAlertOutlierDetector.id())
            .title("Test Alert")
            .window("1h")
            .build());

    }
}
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resources:
  testAlertOutlierDetector:
    type: grafana:machineLearning:OutlierDetector
    name: test_alert_outlier_detector
    properties:
      name: Test Outlier
      metric: tf_test_alert_outlier
      datasourceType: prometheus
      datasourceUid: AbCd12345
      queryParams:
        expr: grafanacloud_grafana_instance_active_user_count
      interval: 300
      algorithm:
        name: dbscan
        sensitivity: 0.5
        config:
          epsilon: 1
  testOutlierAlert:
    type: grafana:machineLearning:Alert
    name: test_outlier_alert
    properties:
      outlierId: ${testAlertOutlierDetector.id}
      title: Test Alert
      window: 1h
Copy

Create Alert Resource

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

Constructor syntax

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

@overload
def Alert(resource_name: str,
          opts: Optional[ResourceOptions] = None,
          title: Optional[str] = None,
          annotations: Optional[Mapping[str, str]] = None,
          anomaly_condition: Optional[str] = None,
          for_: Optional[str] = None,
          job_id: Optional[str] = None,
          labels: Optional[Mapping[str, str]] = None,
          no_data_state: Optional[str] = None,
          outlier_id: Optional[str] = None,
          threshold: Optional[str] = None,
          window: Optional[str] = None)
func NewAlert(ctx *Context, name string, args AlertArgs, opts ...ResourceOption) (*Alert, error)
public Alert(string name, AlertArgs args, CustomResourceOptions? opts = null)
public Alert(String name, AlertArgs args)
public Alert(String name, AlertArgs args, CustomResourceOptions options)

type: grafana:machineLearning:Alert
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. AlertArgs
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. AlertArgs
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. AlertArgs
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. AlertArgs
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. AlertArgs
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 alertResource = new Grafana.MachineLearning.Alert("alertResource", new()
{
    Title = "string",
    Annotations = 
    {
        { "string", "string" },
    },
    AnomalyCondition = "string",
    For = "string",
    JobId = "string",
    Labels = 
    {
        { "string", "string" },
    },
    NoDataState = "string",
    OutlierId = "string",
    Threshold = "string",
    Window = "string",
});
Copy
example, err := machinelearning.NewAlert(ctx, "alertResource", &machinelearning.AlertArgs{
	Title: pulumi.String("string"),
	Annotations: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	AnomalyCondition: pulumi.String("string"),
	For:              pulumi.String("string"),
	JobId:            pulumi.String("string"),
	Labels: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	NoDataState: pulumi.String("string"),
	OutlierId:   pulumi.String("string"),
	Threshold:   pulumi.String("string"),
	Window:      pulumi.String("string"),
})
Copy
var alertResource = new Alert("alertResource", AlertArgs.builder()
    .title("string")
    .annotations(Map.of("string", "string"))
    .anomalyCondition("string")
    .for_("string")
    .jobId("string")
    .labels(Map.of("string", "string"))
    .noDataState("string")
    .outlierId("string")
    .threshold("string")
    .window("string")
    .build());
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alert_resource = grafana.machine_learning.Alert("alertResource",
    title="string",
    annotations={
        "string": "string",
    },
    anomaly_condition="string",
    for_="string",
    job_id="string",
    labels={
        "string": "string",
    },
    no_data_state="string",
    outlier_id="string",
    threshold="string",
    window="string")
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const alertResource = new grafana.machinelearning.Alert("alertResource", {
    title: "string",
    annotations: {
        string: "string",
    },
    anomalyCondition: "string",
    "for": "string",
    jobId: "string",
    labels: {
        string: "string",
    },
    noDataState: "string",
    outlierId: "string",
    threshold: "string",
    window: "string",
});
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type: grafana:machineLearning:Alert
properties:
    annotations:
        string: string
    anomalyCondition: string
    for: string
    jobId: string
    labels:
        string: string
    noDataState: string
    outlierId: string
    threshold: string
    title: string
    window: string
Copy

Alert 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 Alert resource accepts the following input properties:

Title This property is required. string
The title of the alert.
Annotations Dictionary<string, string>
Annotations to add to the alert generated in Grafana.
AnomalyCondition string
The condition for when to consider a point as anomalous.
For string
How long values must be anomalous before firing an alert.
JobId string
The forecast this alert belongs to.
Labels Dictionary<string, string>
Labels to add to the alert generated in Grafana.
NoDataState string
How the alert should be processed when no data is returned by the underlying series
OutlierId string
The forecast this alert belongs to.
Threshold string
The threshold of points over the window that need to be anomalous to alert.
Window string
How much time to average values over
Title This property is required. string
The title of the alert.
Annotations map[string]string
Annotations to add to the alert generated in Grafana.
AnomalyCondition string
The condition for when to consider a point as anomalous.
For string
How long values must be anomalous before firing an alert.
JobId string
The forecast this alert belongs to.
Labels map[string]string
Labels to add to the alert generated in Grafana.
NoDataState string
How the alert should be processed when no data is returned by the underlying series
OutlierId string
The forecast this alert belongs to.
Threshold string
The threshold of points over the window that need to be anomalous to alert.
Window string
How much time to average values over
title This property is required. String
The title of the alert.
annotations Map<String,String>
Annotations to add to the alert generated in Grafana.
anomalyCondition String
The condition for when to consider a point as anomalous.
for_ String
How long values must be anomalous before firing an alert.
jobId String
The forecast this alert belongs to.
labels Map<String,String>
Labels to add to the alert generated in Grafana.
noDataState String
How the alert should be processed when no data is returned by the underlying series
outlierId String
The forecast this alert belongs to.
threshold String
The threshold of points over the window that need to be anomalous to alert.
window String
How much time to average values over
title This property is required. string
The title of the alert.
annotations {[key: string]: string}
Annotations to add to the alert generated in Grafana.
anomalyCondition string
The condition for when to consider a point as anomalous.
for string
How long values must be anomalous before firing an alert.
jobId string
The forecast this alert belongs to.
labels {[key: string]: string}
Labels to add to the alert generated in Grafana.
noDataState string
How the alert should be processed when no data is returned by the underlying series
outlierId string
The forecast this alert belongs to.
threshold string
The threshold of points over the window that need to be anomalous to alert.
window string
How much time to average values over
title This property is required. str
The title of the alert.
annotations Mapping[str, str]
Annotations to add to the alert generated in Grafana.
anomaly_condition str
The condition for when to consider a point as anomalous.
for_ str
How long values must be anomalous before firing an alert.
job_id str
The forecast this alert belongs to.
labels Mapping[str, str]
Labels to add to the alert generated in Grafana.
no_data_state str
How the alert should be processed when no data is returned by the underlying series
outlier_id str
The forecast this alert belongs to.
threshold str
The threshold of points over the window that need to be anomalous to alert.
window str
How much time to average values over
title This property is required. String
The title of the alert.
annotations Map<String>
Annotations to add to the alert generated in Grafana.
anomalyCondition String
The condition for when to consider a point as anomalous.
for String
How long values must be anomalous before firing an alert.
jobId String
The forecast this alert belongs to.
labels Map<String>
Labels to add to the alert generated in Grafana.
noDataState String
How the alert should be processed when no data is returned by the underlying series
outlierId String
The forecast this alert belongs to.
threshold String
The threshold of points over the window that need to be anomalous to alert.
window String
How much time to average values over

Outputs

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

Get an existing Alert 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?: AlertState, opts?: CustomResourceOptions): Alert
@staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        annotations: Optional[Mapping[str, str]] = None,
        anomaly_condition: Optional[str] = None,
        for_: Optional[str] = None,
        job_id: Optional[str] = None,
        labels: Optional[Mapping[str, str]] = None,
        no_data_state: Optional[str] = None,
        outlier_id: Optional[str] = None,
        threshold: Optional[str] = None,
        title: Optional[str] = None,
        window: Optional[str] = None) -> Alert
func GetAlert(ctx *Context, name string, id IDInput, state *AlertState, opts ...ResourceOption) (*Alert, error)
public static Alert Get(string name, Input<string> id, AlertState? state, CustomResourceOptions? opts = null)
public static Alert get(String name, Output<String> id, AlertState state, CustomResourceOptions options)
resources:  _:    type: grafana:machineLearning:Alert    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:
Annotations Dictionary<string, string>
Annotations to add to the alert generated in Grafana.
AnomalyCondition string
The condition for when to consider a point as anomalous.
For string
How long values must be anomalous before firing an alert.
JobId string
The forecast this alert belongs to.
Labels Dictionary<string, string>
Labels to add to the alert generated in Grafana.
NoDataState string
How the alert should be processed when no data is returned by the underlying series
OutlierId string
The forecast this alert belongs to.
Threshold string
The threshold of points over the window that need to be anomalous to alert.
Title string
The title of the alert.
Window string
How much time to average values over
Annotations map[string]string
Annotations to add to the alert generated in Grafana.
AnomalyCondition string
The condition for when to consider a point as anomalous.
For string
How long values must be anomalous before firing an alert.
JobId string
The forecast this alert belongs to.
Labels map[string]string
Labels to add to the alert generated in Grafana.
NoDataState string
How the alert should be processed when no data is returned by the underlying series
OutlierId string
The forecast this alert belongs to.
Threshold string
The threshold of points over the window that need to be anomalous to alert.
Title string
The title of the alert.
Window string
How much time to average values over
annotations Map<String,String>
Annotations to add to the alert generated in Grafana.
anomalyCondition String
The condition for when to consider a point as anomalous.
for_ String
How long values must be anomalous before firing an alert.
jobId String
The forecast this alert belongs to.
labels Map<String,String>
Labels to add to the alert generated in Grafana.
noDataState String
How the alert should be processed when no data is returned by the underlying series
outlierId String
The forecast this alert belongs to.
threshold String
The threshold of points over the window that need to be anomalous to alert.
title String
The title of the alert.
window String
How much time to average values over
annotations {[key: string]: string}
Annotations to add to the alert generated in Grafana.
anomalyCondition string
The condition for when to consider a point as anomalous.
for string
How long values must be anomalous before firing an alert.
jobId string
The forecast this alert belongs to.
labels {[key: string]: string}
Labels to add to the alert generated in Grafana.
noDataState string
How the alert should be processed when no data is returned by the underlying series
outlierId string
The forecast this alert belongs to.
threshold string
The threshold of points over the window that need to be anomalous to alert.
title string
The title of the alert.
window string
How much time to average values over
annotations Mapping[str, str]
Annotations to add to the alert generated in Grafana.
anomaly_condition str
The condition for when to consider a point as anomalous.
for_ str
How long values must be anomalous before firing an alert.
job_id str
The forecast this alert belongs to.
labels Mapping[str, str]
Labels to add to the alert generated in Grafana.
no_data_state str
How the alert should be processed when no data is returned by the underlying series
outlier_id str
The forecast this alert belongs to.
threshold str
The threshold of points over the window that need to be anomalous to alert.
title str
The title of the alert.
window str
How much time to average values over
annotations Map<String>
Annotations to add to the alert generated in Grafana.
anomalyCondition String
The condition for when to consider a point as anomalous.
for String
How long values must be anomalous before firing an alert.
jobId String
The forecast this alert belongs to.
labels Map<String>
Labels to add to the alert generated in Grafana.
noDataState String
How the alert should be processed when no data is returned by the underlying series
outlierId String
The forecast this alert belongs to.
threshold String
The threshold of points over the window that need to be anomalous to alert.
title String
The title of the alert.
window String
How much time to average values over

Import

$ pulumi import grafana:machineLearning/alert:Alert name "{{ id }}"
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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 v0.16.3 published on Monday, Apr 7, 2025 by pulumiverse
pulumiverse/pulumi-grafana