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Add resources for creating ML managed alerts
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| --- | ||
| # generated by https://github.com/hashicorp/terraform-plugin-docs | ||
| page_title: "grafana_machine_learning_alert Resource - terraform-provider-grafana" | ||
| subcategory: "Machine Learning" | ||
| description: |- | ||
| A job defines the queries and model parameters for a machine learning task. | ||
| --- | ||
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| # grafana_machine_learning_alert (Resource) | ||
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| A job defines the queries and model parameters for a machine learning task. | ||
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| <!-- schema generated by tfplugindocs --> | ||
| ## Schema | ||
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| ### Required | ||
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| - `title` (String) The title of the alert. | ||
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| ### Optional | ||
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| - `annotations` (Map of String) Annotations to add to the alert generated in Grafana. | ||
| - `anomaly_condition` (String) The condition for when to consider a point as anomalous. | ||
| - `for` (String) How long values must be anomalous before firing an alert. | ||
| - `job_id` (String) The forecast this alert belongs to. | ||
| - `labels` (Map of String) Labels to add to the alert generated in Grafana. | ||
| - `no_data_state` (String) How the alert should be processed when no data is returned by the underlying series | ||
| - `outlier_id` (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 | ||
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| ### Read-Only | ||
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| - `id` (String) The ID of the alert. | ||
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| ## Import | ||
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| Import is supported using the following syntax: | ||
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| ```shell | ||
| terraform import grafana_machine_learning_alert.name "{{ id }}" | ||
| ``` |
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| terraform import grafana_machine_learning_alert.name "{{ id }}" |
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36
examples/resources/grafana_machine_learning_alert/job_alert.tf
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| resource "grafana_data_source" "foo" { | ||
| type = "prometheus" | ||
| name = "prometheus-ds-test" | ||
| uid = "prometheus-ds-test-uid" | ||
| url = "https://my-instance.com" | ||
| basic_auth_enabled = true | ||
| basic_auth_username = "username" | ||
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| json_data_encoded = jsonencode({ | ||
| httpMethod = "POST" | ||
| prometheusType = "Mimir" | ||
| prometheusVersion = "2.4.0" | ||
| }) | ||
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| secure_json_data_encoded = jsonencode({ | ||
| basicAuthPassword = "password" | ||
| }) | ||
| } | ||
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| resource "grafana_machine_learning_job" "test_job" { | ||
| name = "Test Job" | ||
| metric = "tf_test_job" | ||
| datasource_type = "prometheus" | ||
| datasource_uid = grafana_data_source.foo.uid | ||
| query_params = { | ||
| expr = "grafanacloud_grafana_instance_active_user_count" | ||
| } | ||
| } | ||
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| resource "grafana_machine_learning_alert" "test_job_alert" { | ||
| job_id = grafana_machine_learning_job.test_job.id | ||
| title = "Test Alert" | ||
| anomaly_condition = "any" | ||
| threshold = ">0.8" | ||
| window = "15m" | ||
| } |
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examples/resources/grafana_machine_learning_alert/outlier_alert.tf
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| resource "grafana_machine_learning_outlier_detector" "my_dbscan_outlier_detector" { | ||
| name = "My DBSCAN outlier detector" | ||
| description = "My DBSCAN Outlier Detector" | ||
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| metric = "tf_test_dbscan_job" | ||
| datasource_type = "prometheus" | ||
| datasource_uid = "AbCd12345" | ||
| query_params = { | ||
| expr = "grafanacloud_grafana_instance_active_user_count" | ||
| } | ||
| interval = 300 | ||
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| algorithm { | ||
| name = "dbscan" | ||
| sensitivity = 0.5 | ||
| config { | ||
| epsilon = 1.0 | ||
| } | ||
| } | ||
| } | ||
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| resource "grafana_machine_learning_alert" "test_job_alert" { | ||
| outlier_id = grafana_machine_learning_outlier_detector.my_dbscan_outlier_detector.id | ||
| title = "Test Alert" | ||
| window = "1h" | ||
| } |
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| package machinelearning | ||
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| import ( | ||
| "context" | ||
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| "github.com/grafana/machine-learning-go-client/mlapi" | ||
| "github.com/grafana/terraform-provider-grafana/v3/internal/common" | ||
| "github.com/hashicorp/terraform-plugin-sdk/v2/diag" | ||
| "github.com/hashicorp/terraform-plugin-sdk/v2/helper/schema" | ||
| "github.com/hashicorp/terraform-plugin-sdk/v2/helper/validation" | ||
| "github.com/prometheus/common/model" | ||
| ) | ||
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| var resourceAlertID = common.NewResourceID(common.StringIDField("id")) | ||
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| func resourceAlert() *common.Resource { | ||
| schema := &schema.Resource{ | ||
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| Description: ` | ||
| A job defines the queries and model parameters for a machine learning task. | ||
| `, | ||
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| CreateContext: checkClient(resourceAlertCreate), | ||
| ReadContext: checkClient(resourceAlertRead), | ||
| UpdateContext: checkClient(resourceAlertUpdate), | ||
| DeleteContext: checkClient(resourceAlertDelete), | ||
| Importer: &schema.ResourceImporter{ | ||
| StateContext: schema.ImportStatePassthroughContext, | ||
| }, | ||
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| Schema: map[string]*schema.Schema{ | ||
| "job_id": { | ||
| Description: "The forecast this alert belongs to.", | ||
| Type: schema.TypeString, | ||
| Optional: true, | ||
| ForceNew: true, | ||
| ExactlyOneOf: []string{"job_id", "outlier_id"}, | ||
| }, | ||
| "outlier_id": { | ||
| Description: "The forecast this alert belongs to.", | ||
| Type: schema.TypeString, | ||
| Optional: true, | ||
| ForceNew: true, | ||
| ExactlyOneOf: []string{"job_id", "outlier_id"}, | ||
| }, | ||
| "id": { | ||
| Description: "The ID of the alert.", | ||
| Type: schema.TypeString, | ||
| Computed: true, | ||
| }, | ||
| "title": { | ||
| Description: "The title of the alert.", | ||
| Type: schema.TypeString, | ||
| Required: true, | ||
| }, | ||
| "anomaly_condition": { | ||
| Description: "The condition for when to consider a point as anomalous.", | ||
| Type: schema.TypeString, | ||
| Optional: true, | ||
| ValidateFunc: validation.StringInSlice([]string{"any", "low", "high"}, false), | ||
| }, | ||
| "for": { | ||
| Description: "How long values must be anomalous before firing an alert.", | ||
| Type: schema.TypeString, | ||
| Optional: true, | ||
| }, | ||
| "threshold": { | ||
| Description: "The threshold of points over the window that need to be anomalous to alert.", | ||
| Type: schema.TypeString, | ||
| Optional: true, | ||
| }, | ||
| "window": { | ||
| Description: "How much time to average values over", | ||
| Type: schema.TypeString, | ||
| Optional: true, | ||
| }, | ||
| "labels": { | ||
| Description: "Labels to add to the alert generated in Grafana.", | ||
| Type: schema.TypeMap, | ||
| Optional: true, | ||
| }, | ||
| "annotations": { | ||
| Description: "Annotations to add to the alert generated in Grafana.", | ||
| Type: schema.TypeMap, | ||
| Optional: true, | ||
| }, | ||
| "no_data_state": { | ||
| Description: "How the alert should be processed when no data is returned by the underlying series", | ||
| Type: schema.TypeString, | ||
| Optional: true, | ||
| ValidateFunc: validation.StringInSlice([]string{"Alerting", "NoData", "OK"}, false), | ||
| }, | ||
| }, | ||
| } | ||
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| return common.NewLegacySDKResource( | ||
| common.CategoryMachineLearning, | ||
| "grafana_machine_learning_alert", | ||
| resourceAlertID, | ||
| schema, | ||
| ) | ||
| } | ||
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| func resourceAlertCreate(ctx context.Context, d *schema.ResourceData, meta interface{}) diag.Diagnostics { | ||
| c := meta.(*common.Client).MLAPI | ||
| alert, err := makeMLAlert(d) | ||
| if err != nil { | ||
| return diag.FromErr(err) | ||
| } | ||
| jobID := d.Get("job_id").(string) | ||
| if jobID != "" { | ||
| alert, err = c.NewJobAlert(ctx, jobID, alert) | ||
| } else { | ||
| outlierID := d.Get("outlier_id").(string) | ||
| alert, err = c.NewOutlierAlert(ctx, outlierID, alert) | ||
| } | ||
| if err != nil { | ||
| return diag.FromErr(err) | ||
| } | ||
| d.SetId(alert.ID) | ||
| return resourceAlertRead(ctx, d, meta) | ||
| } | ||
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| func resourceAlertRead(ctx context.Context, d *schema.ResourceData, meta interface{}) diag.Diagnostics { | ||
| c := meta.(*common.Client).MLAPI | ||
| var ( | ||
| alert mlapi.Alert | ||
| err error | ||
| ) | ||
| jobID := d.Get("job_id").(string) | ||
| if jobID != "" { | ||
| alert, err = c.JobAlert(ctx, jobID, d.Id()) | ||
| } else { | ||
| outlierID := d.Get("outlier_id").(string) | ||
| alert, err = c.OutlierAlert(ctx, outlierID, d.Id()) | ||
| } | ||
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| if err, shouldReturn := common.CheckReadError("alert", d, err); shouldReturn { | ||
| return err | ||
| } | ||
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| d.Set("title", alert.Title) | ||
| d.Set("anomaly_condition", alert.AnomalyCondition) | ||
| d.Set("for", alert.For) | ||
| d.Set("threshold", alert.Threshold) | ||
| d.Set("window", alert.Window) | ||
| d.Set("labels", alert.Labels) | ||
| d.Set("annotations", alert.Annotations) | ||
| d.Set("no_data_state", alert.NoDataState) | ||
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| return nil | ||
| } | ||
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| func resourceAlertUpdate(ctx context.Context, d *schema.ResourceData, meta interface{}) diag.Diagnostics { | ||
| c := meta.(*common.Client).MLAPI | ||
| alert, err := makeMLAlert(d) | ||
| if err != nil { | ||
| return diag.FromErr(err) | ||
| } | ||
| jobID := d.Get("job_id").(string) | ||
| if jobID != "" { | ||
| _, err = c.UpdateJobAlert(ctx, jobID, alert) | ||
| } else { | ||
| outlierID := d.Get("outlier_id").(string) | ||
| _, err = c.UpdateOutlierAlert(ctx, outlierID, alert) | ||
| } | ||
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| if err != nil { | ||
| return diag.FromErr(err) | ||
| } | ||
| return resourceAlertRead(ctx, d, meta) | ||
| } | ||
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| func resourceAlertDelete(ctx context.Context, d *schema.ResourceData, meta interface{}) diag.Diagnostics { | ||
| c := meta.(*common.Client).MLAPI | ||
| jobID := d.Get("job_id").(string) | ||
| var err error | ||
| if jobID != "" { | ||
| err = c.DeleteJobAlert(ctx, jobID, d.Id()) | ||
| } else { | ||
| outlierID := d.Get("outlier_id").(string) | ||
| err = c.DeleteOutlierAlert(ctx, outlierID, d.Id()) | ||
| } | ||
| return diag.FromErr(err) | ||
| } | ||
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| func makeMLAlert(d *schema.ResourceData) (mlapi.Alert, error) { | ||
| forClause, err := parseDuration(d.Get("for").(string)) | ||
| if err != nil { | ||
| return mlapi.Alert{}, err | ||
| } | ||
| window, err := parseDuration(d.Get("window").(string)) | ||
| if err != nil { | ||
| return mlapi.Alert{}, err | ||
| } | ||
| labels := map[string]string{} | ||
| for k, v := range d.Get("labels").(map[string]interface{}) { | ||
| labels[k] = v.(string) | ||
| } | ||
| annotations := map[string]string{} | ||
| for k, v := range d.Get("annotations").(map[string]interface{}) { | ||
| annotations[k] = v.(string) | ||
| } | ||
| return mlapi.Alert{ | ||
| ID: d.Id(), | ||
| Title: d.Get("title").(string), | ||
| AnomalyCondition: mlapi.AnomalyCondition(d.Get("anomaly_condition").(string)), | ||
| For: forClause, | ||
| Threshold: d.Get("threshold").(string), | ||
| Window: window, | ||
| Labels: labels, | ||
| Annotations: annotations, | ||
| NoDataState: mlapi.NoDataState(d.Get("no_data_state").(string)), | ||
| }, nil | ||
| } | ||
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| func parseDuration(s string) (model.Duration, error) { | ||
| if s == "" { | ||
| return 0, nil | ||
| } | ||
| return model.ParseDuration(s) | ||
| } |
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