Merge branch 'main' of github.com:grafana/terraform-provider-grafana …

…into tf-cloud-provider-aws-app

docs/resources/machine_learning_alert.md

@@ -12,6 +12,10 @@ description: |-
 
 ## Example Usage
 
+### Forecast Alert
+
+This alert uses a forecast.
+
 ```terraform
 resource "grafana_machine_learning_job" "test_alert_job" {
   name            = "Test Job"
@@ -32,28 +36,37 @@ resource "grafana_machine_learning_alert" "test_job_alert" {
 }
 ```
 
-<!-- schema generated by tfplugindocs -->
-## Schema
-
-### Required
-
-- `title` (String) The title of the alert.
+### Outlier Alert
 
-### Optional
+This alert uses an outlier detector.
 
-- `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
+```terraform
+resource "grafana_machine_learning_outlier_detector" "test_alert_outlier_detector" {
+  name = "Test Outlier"
 
-### Read-Only
+  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 = 1.0
+    }
+  }
+}
 
-- `id` (String) The ID of the alert.
+resource "grafana_machine_learning_alert" "test_outlier_alert" {
+  outlier_id = grafana_machine_learning_outlier_detector.test_alert_outlier_detector.id
+  title      = "Test Alert"
+  window     = "1h"
+}
+```
 
 ## Import
 

docs/resources/machine_learning_holiday.md

@@ -29,37 +29,43 @@ resource "grafana_machine_learning_job" "test_job" {
 }
 ```
 
+## Example Usage
 
+### iCal Holiday
 
-<!-- schema generated by tfplugindocs -->
-## Schema
+This holiday uses an iCal file to define the holidays.
 
-### Required
-
-- `name` (String) The name of the holiday.
-
-### Optional
-
-- `custom_periods` (Block List) A list of custom periods for the holiday. (see [below for nested schema](#nestedblock--custom_periods))
-- `description` (String) A description of the holiday.
-- `ical_timezone` (String) The timezone to use for events in the iCal file pointed to by ical_url.
-- `ical_url` (String) A URL to an iCal file containing all occurrences of the holiday.
-
-### Read-Only
-
-- `id` (String) The ID of the holiday.
-
-<a id="nestedblock--custom_periods"></a>
-### Nested Schema for `custom_periods`
+```terraform
+resource "grafana_machine_learning_holiday" "ical" {
+  name        = "My iCal holiday"
+  description = "My Holiday"
 
-Required:
+  ical_url      = "https://calendar.google.com/calendar/ical/en.uk%23holiday%40group.v.calendar.google.com/public/basic.ics"
+  ical_timezone = "Europe/London"
+}
+```
 
-- `end_time` (String)
-- `start_time` (String)
+### Custom Periods Holiday
 
-Optional:
+This holiday uses custom periods to define the holidays.
 
-- `name` (String) The name of the custom period.
+```terraform
+resource "grafana_machine_learning_holiday" "custom_periods" {
+  name        = "My custom periods holiday"
+  description = "My Holiday"
+
+  custom_periods {
+    name       = "First of January"
+    start_time = "2023-01-01T00:00:00Z"
+    end_time   = "2023-01-02T00:00:00Z"
+  }
+  custom_periods {
+    name       = "First of Feburary"
+    start_time = "2023-02-01T00:00:00Z"
+    end_time   = "2023-02-02T00:00:00Z"
+  }
+}
+```
 
 ## Import
 

docs/resources/machine_learning_job.md

@@ -10,31 +10,133 @@ description: |-
 
 A job defines the queries and model parameters for a machine learning task.
 
+## Example Usage
 
+### Basic Forecast
 
-<!-- schema generated by tfplugindocs -->
-## Schema
+This forecast uses a Prometheus datasource, where the source query is defined in the `expr` field of the `query_params` attribute.
 
-### Required
+Other datasources are supported, but the structure `query_params` may differ.
 
-- `datasource_type` (String) The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
-- `datasource_uid` (String) The uid of the datasource to query.
-- `metric` (String) The metric used to query the job results.
-- `name` (String) The name of the job.
-- `query_params` (Map of String) An object representing the query params to query Grafana with.
+```terraform
+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"
 
-### Optional
+  json_data_encoded = jsonencode({
+    httpMethod        = "POST"
+    prometheusType    = "Mimir"
+    prometheusVersion = "2.4.0"
+  })
 
-- `custom_labels` (Map of String) An object representing the custom labels added on the forecast.
-- `description` (String) A description of the job.
-- `holidays` (List of String) A list of holiday IDs or names to take into account when training the model.
-- `hyper_params` (Map of String) The hyperparameters used to fine tune the algorithm. See https://grafana.com/docs/grafana-cloud/machine-learning/models/ for the full list of available hyperparameters. Defaults to `map[]`.
-- `interval` (Number) The data interval in seconds to train the data on. Defaults to `300`.
-- `training_window` (Number) The data interval in seconds to train the data on. Defaults to `7776000`.
+  secure_json_data_encoded = jsonencode({
+    basicAuthPassword = "password"
+  })
+}
 
-### Read-Only
+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"
+  }
+}
+```
+
+### Tuned Forecast
+
+This forecast has tuned hyperparameters to improve the accuracy of the model.
+
+```terraform
+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"
+
+  json_data_encoded = jsonencode({
+    httpMethod        = "POST"
+    prometheusType    = "Mimir"
+    prometheusVersion = "2.4.0"
+  })
+
+  secure_json_data_encoded = jsonencode({
+    basicAuthPassword = "password"
+  })
+}
+
+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"
+  }
+  hyper_params = {
+    daily_seasonality  = 15
+    weekly_seasonality = 10
+  }
+  custom_labels = {
+    example_label = "example_value"
+  }
+}
+```
 
-- `id` (String) The ID of the job.
+### Forecast with Holidays
+
+This forecast has holidays which will be taken into account when training the model.
+
+```terraform
+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"
+
+  json_data_encoded = jsonencode({
+    httpMethod        = "POST"
+    prometheusType    = "Mimir"
+    prometheusVersion = "2.4.0"
+  })
+
+  secure_json_data_encoded = jsonencode({
+    basicAuthPassword = "password"
+  })
+}
+
+resource "grafana_machine_learning_holiday" "test_holiday" {
+  name = "Test Holiday"
+  custom_periods {
+    name       = "First of January"
+    start_time = "2023-01-01T00:00:00Z"
+    end_time   = "2023-01-02T00:00:00Z"
+  }
+}
+
+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"
+  }
+  holidays = [
+    grafana_machine_learning_holiday.test_holiday.id
+  ]
+}
+```
 
 ## Import
 

docs/resources/machine_learning_outlier_detector.md

@@ -16,47 +16,58 @@ The normal band is configured by choice of algorithm, its sensitivity and other
 
 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.
+
+```terraform
+resource "grafana_machine_learning_outlier_detector" "my_dbscan_outlier_detector" {
+  name        = "My DBSCAN outlier detector"
+  description = "My DBSCAN Outlier Detector"
+
+  metric          = "tf_test_dbscan_job"
+  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 = 1.0
+    }
+  }
+}
+```
 
+### MAD Outlier Detector
 
-<!-- schema generated by tfplugindocs -->
-## Schema
-
-### Required
-
-- `algorithm` (Block Set, Min: 1, Max: 1) The algorithm to use and its configuration. See https://grafana.com/docs/grafana-cloud/machine-learning/outlier-detection/ for details. (see [below for nested schema](#nestedblock--algorithm))
-- `datasource_type` (String) The type of datasource being queried. Currently allowed values are prometheus, graphite, loki, postgres, and datadog.
-- `datasource_uid` (String) The uid of the datasource to query.
-- `metric` (String) The metric used to query the outlier detector results.
-- `name` (String) The name of the outlier detector.
-- `query_params` (Map of String) An object representing the query params to query Grafana with.
-
-### Optional
-
-- `description` (String) A description of the outlier detector.
-- `interval` (Number) The data interval in seconds to monitor. Defaults to `300`.
-
-### Read-Only
-
-- `id` (String) The ID of the outlier detector.
-
-<a id="nestedblock--algorithm"></a>
-### Nested Schema for `algorithm`
-
-Required:
-
-- `name` (String) The name of the algorithm to use ('mad' or 'dbscan').
-- `sensitivity` (Number) Specify the sensitivity of the detector (in range [0,1]).
-
-Optional:
-
-- `config` (Block Set, Max: 1) For DBSCAN only, specify the configuration map (see [below for nested schema](#nestedblock--algorithm--config))
+This outlier detector uses the Median Absolute Deviation (MAD) algorithm to detect outliers.
 
-<a id="nestedblock--algorithm--config"></a>
-### Nested Schema for `algorithm.config`
+```terraform
+resource "grafana_machine_learning_outlier_detector" "my_mad_outlier_detector" {
+  name        = "My MAD outlier detector"
+  description = "My MAD Outlier Detector"
 
-Required:
+  metric          = "tf_test_mad_job"
+  datasource_type = "prometheus"
+  datasource_uid  = "AbCd12345"
+  query_params = {
+    expr = "grafanacloud_grafana_instance_active_user_count"
+  }
+  interval = 300
 
-- `epsilon` (Number) Specify the epsilon parameter (positive float)
+  algorithm {
+    name        = "mad"
+    sensitivity = 0.7
+  }
+}
+```
 
 ## Import