@lbrlabs/pulumi-grafana
Version:
A Pulumi package for creating and managing grafana.
84 lines • 4.59 kB
JavaScript
;
// *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
// *** Do not edit by hand unless you're certain you know what you are doing! ***
Object.defineProperty(exports, "__esModule", { value: true });
exports.MachineLearningOutlierDetector = 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.
*/
class MachineLearningOutlierDetector extends pulumi.CustomResource {
/**
* Get an existing MachineLearningOutlierDetector 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 MachineLearningOutlierDetector(name, state, Object.assign(Object.assign({}, opts), { id: id }));
}
/**
* Returns true if the given object is an instance of MachineLearningOutlierDetector. 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'] === MachineLearningOutlierDetector.__pulumiType;
}
constructor(name, argsOrState, opts) {
let resourceInputs = {};
opts = opts || {};
if (opts.id) {
const state = argsOrState;
resourceInputs["algorithm"] = state ? state.algorithm : undefined;
resourceInputs["datasourceId"] = state ? state.datasourceId : undefined;
resourceInputs["datasourceType"] = state ? state.datasourceType : undefined;
resourceInputs["datasourceUid"] = state ? state.datasourceUid : undefined;
resourceInputs["description"] = state ? state.description : undefined;
resourceInputs["interval"] = state ? state.interval : undefined;
resourceInputs["metric"] = state ? state.metric : undefined;
resourceInputs["name"] = state ? state.name : undefined;
resourceInputs["queryParams"] = state ? state.queryParams : undefined;
}
else {
const args = argsOrState;
if ((!args || args.algorithm === undefined) && !opts.urn) {
throw new Error("Missing required property 'algorithm'");
}
if ((!args || args.datasourceType === undefined) && !opts.urn) {
throw new Error("Missing required property 'datasourceType'");
}
if ((!args || args.metric === undefined) && !opts.urn) {
throw new Error("Missing required property 'metric'");
}
if ((!args || args.queryParams === undefined) && !opts.urn) {
throw new Error("Missing required property 'queryParams'");
}
resourceInputs["algorithm"] = args ? args.algorithm : undefined;
resourceInputs["datasourceId"] = args ? args.datasourceId : undefined;
resourceInputs["datasourceType"] = args ? args.datasourceType : undefined;
resourceInputs["datasourceUid"] = args ? args.datasourceUid : undefined;
resourceInputs["description"] = args ? args.description : undefined;
resourceInputs["interval"] = args ? args.interval : undefined;
resourceInputs["metric"] = args ? args.metric : undefined;
resourceInputs["name"] = args ? args.name : undefined;
resourceInputs["queryParams"] = args ? args.queryParams : undefined;
}
opts = pulumi.mergeOptions(utilities.resourceOptsDefaults(), opts);
super(MachineLearningOutlierDetector.__pulumiType, name, resourceInputs, opts);
}
}
exports.MachineLearningOutlierDetector = MachineLearningOutlierDetector;
/** @internal */
MachineLearningOutlierDetector.__pulumiType = 'grafana:index/machineLearningOutlierDetector:MachineLearningOutlierDetector';
//# sourceMappingURL=machineLearningOutlierDetector.js.map