@pulumiverse/grafana
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A Pulumi package for creating and managing grafana.
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JavaScript
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// *** WARNING: this file was generated by pulumi-language-nodejs. ***
// *** Do not edit by hand unless you're certain you know what you are doing! ***
Object.defineProperty(exports, "__esModule", { value: true });
exports.OutlierDetector = void 0;
const pulumi = require("@pulumi/pulumi");
const utilities = require("../utilities");
/**
* 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.
*
* ## Example Usage
*
* ### DBSCAN Outlier Detector
*
* This outlier detector uses the DBSCAN algorithm to detect outliers.
*
* ```typescript
* import * as pulumi from "@pulumi/pulumi";
* import * as grafana from "@pulumiverse/grafana";
*
* const myDbscanOutlierDetector = new grafana.machinelearning.OutlierDetector("my_dbscan_outlier_detector", {
* name: "My DBSCAN outlier detector",
* description: "My DBSCAN Outlier Detector",
* metric: "tf_test_dbscan_job",
* datasourceType: "prometheus",
* datasourceUid: "AbCd12345",
* queryParams: {
* expr: "grafanacloud_grafana_instance_active_user_count",
* },
* interval: 300,
* algorithm: {
* name: "dbscan",
* sensitivity: 0.5,
* config: {
* epsilon: 1,
* },
* },
* });
* ```
*
* ### MAD Outlier Detector
*
* This outlier detector uses the Median Absolute Deviation (MAD) algorithm to detect outliers.
*
* ```typescript
* import * as pulumi from "@pulumi/pulumi";
* import * as grafana from "@pulumiverse/grafana";
*
* const myMadOutlierDetector = new grafana.machinelearning.OutlierDetector("my_mad_outlier_detector", {
* name: "My MAD outlier detector",
* description: "My MAD Outlier Detector",
* metric: "tf_test_mad_job",
* datasourceType: "prometheus",
* datasourceUid: "AbCd12345",
* queryParams: {
* expr: "grafanacloud_grafana_instance_active_user_count",
* },
* interval: 300,
* algorithm: {
* name: "mad",
* sensitivity: 0.7,
* },
* });
* ```
*
* ## Import
*
* ```sh
* terraform import grafana_machine_learning_outlier_detector.name "{{ id }}"
* ```
*/
class OutlierDetector extends pulumi.CustomResource {
/**
* Get an existing OutlierDetector resource's state with the given name, ID, and optional extra
* properties used to qualify the lookup.
*
* @param name The _unique_ name of the resulting resource.
* @param id The _unique_ provider ID of the resource to lookup.
* @param state Any extra arguments used during the lookup.
* @param opts Optional settings to control the behavior of the CustomResource.
*/
static get(name, id, state, opts) {
return new OutlierDetector(name, state, { ...opts, id: id });
}
/**
* Returns true if the given object is an instance of OutlierDetector. This is designed to work even
* when multiple copies of the Pulumi SDK have been loaded into the same process.
*/
static isInstance(obj) {
if (obj === undefined || obj === null) {
return false;
}
return obj['__pulumiType'] === OutlierDetector.__pulumiType;
}
constructor(name, argsOrState, opts) {
let resourceInputs = {};
opts = opts || {};
if (opts.id) {
const state = argsOrState;
resourceInputs["algorithm"] = state?.algorithm;
resourceInputs["datasourceType"] = state?.datasourceType;
resourceInputs["datasourceUid"] = state?.datasourceUid;
resourceInputs["description"] = state?.description;
resourceInputs["interval"] = state?.interval;
resourceInputs["metric"] = state?.metric;
resourceInputs["name"] = state?.name;
resourceInputs["queryParams"] = state?.queryParams;
}
else {
const args = argsOrState;
if (args?.algorithm === undefined && !opts.urn) {
throw new Error("Missing required property 'algorithm'");
}
if (args?.datasourceType === undefined && !opts.urn) {
throw new Error("Missing required property 'datasourceType'");
}
if (args?.datasourceUid === undefined && !opts.urn) {
throw new Error("Missing required property 'datasourceUid'");
}
if (args?.metric === undefined && !opts.urn) {
throw new Error("Missing required property 'metric'");
}
if (args?.queryParams === undefined && !opts.urn) {
throw new Error("Missing required property 'queryParams'");
}
resourceInputs["algorithm"] = args?.algorithm;
resourceInputs["datasourceType"] = args?.datasourceType;
resourceInputs["datasourceUid"] = args?.datasourceUid;
resourceInputs["description"] = args?.description;
resourceInputs["interval"] = args?.interval;
resourceInputs["metric"] = args?.metric;
resourceInputs["name"] = args?.name;
resourceInputs["queryParams"] = args?.queryParams;
}
opts = pulumi.mergeOptions(utilities.resourceOptsDefaults(), opts);
super(OutlierDetector.__pulumiType, name, resourceInputs, opts);
}
}
exports.OutlierDetector = OutlierDetector;
/** @internal */
OutlierDetector.__pulumiType = 'grafana:machineLearning/outlierDetector:OutlierDetector';
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