import * as pulumi from "@pulumi/pulumi";
import * as inputs from "../types/input";
import * as outputs from "../types/output";
/**
 * 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
 * $ pulumi import grafana:machineLearning/outlierDetector:OutlierDetector name "{{ id }}"
 * ```
 */
export declare 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: string, id: pulumi.Input<pulumi.ID>, state?: OutlierDetectorState, opts?: pulumi.CustomResourceOptions): OutlierDetector;
    /**
     * 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: any): obj is OutlierDetector;
    /**
     * The algorithm to use and its configuration. See
     * https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
     */
    readonly algorithm: pulumi.Output<outputs.machineLearning.OutlierDetectorAlgorithm>;
    /**
     * The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
     */
    readonly datasourceType: pulumi.Output<string>;
    /**
     * The uid of the datasource to query.
     */
    readonly datasourceUid: pulumi.Output<string>;
    /**
     * A description of the outlier detector.
     */
    readonly description: pulumi.Output<string | undefined>;
    /**
     * The data interval in seconds to monitor.
     */
    readonly interval: pulumi.Output<number | undefined>;
    /**
     * The metric used to query the outlier detector results.
     */
    readonly metric: pulumi.Output<string>;
    /**
     * The name of the outlier detector.
     */
    readonly name: pulumi.Output<string>;
    /**
     * An object representing the query params to query Grafana with.
     */
    readonly queryParams: pulumi.Output<{
        [key: string]: string;
    }>;
    /**
     * Create a OutlierDetector resource with the given unique name, arguments, and options.
     *
     * @param name The _unique_ name of the resource.
     * @param args The arguments to use to populate this resource's properties.
     * @param opts A bag of options that control this resource's behavior.
     */
    constructor(name: string, args: OutlierDetectorArgs, opts?: pulumi.CustomResourceOptions);
}
/**
 * Input properties used for looking up and filtering OutlierDetector resources.
 */
export interface OutlierDetectorState {
    /**
     * The algorithm to use and its configuration. See
     * https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
     */
    algorithm?: pulumi.Input<inputs.machineLearning.OutlierDetectorAlgorithm>;
    /**
     * The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
     */
    datasourceType?: pulumi.Input<string>;
    /**
     * The uid of the datasource to query.
     */
    datasourceUid?: pulumi.Input<string>;
    /**
     * A description of the outlier detector.
     */
    description?: pulumi.Input<string>;
    /**
     * The data interval in seconds to monitor.
     */
    interval?: pulumi.Input<number>;
    /**
     * The metric used to query the outlier detector results.
     */
    metric?: pulumi.Input<string>;
    /**
     * The name of the outlier detector.
     */
    name?: pulumi.Input<string>;
    /**
     * An object representing the query params to query Grafana with.
     */
    queryParams?: pulumi.Input<{
        [key: string]: pulumi.Input<string>;
    }>;
}
/**
 * The set of arguments for constructing a OutlierDetector resource.
 */
export interface OutlierDetectorArgs {
    /**
     * The algorithm to use and its configuration. See
     * https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details.
     */
    algorithm: pulumi.Input<inputs.machineLearning.OutlierDetectorAlgorithm>;
    /**
     * The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
     */
    datasourceType: pulumi.Input<string>;
    /**
     * The uid of the datasource to query.
     */
    datasourceUid: pulumi.Input<string>;
    /**
     * A description of the outlier detector.
     */
    description?: pulumi.Input<string>;
    /**
     * The data interval in seconds to monitor.
     */
    interval?: pulumi.Input<number>;
    /**
     * The metric used to query the outlier detector results.
     */
    metric: pulumi.Input<string>;
    /**
     * The name of the outlier detector.
     */
    name?: pulumi.Input<string>;
    /**
     * An object representing the query params to query Grafana with.
     */
    queryParams: pulumi.Input<{
        [key: string]: pulumi.Input<string>;
    }>;
}