Merge branch 'master' into environment-alert

Author: elasticmachine

.eslintrc.js

@@ -112,7 +112,6 @@ module.exports = {
       files: ['x-pack/plugins/lens/**/*.{js,ts,tsx}'],
       rules: {
         'react-hooks/exhaustive-deps': 'off',
-        'react-hooks/rules-of-hooks': 'off',
       },
     },
     {

.i18nrc.json

@@ -34,7 +34,7 @@
     "kibana_utils": "src/plugins/kibana_utils",
     "navigation": "src/plugins/navigation",
     "newsfeed": "src/plugins/newsfeed",
-    "regionMap": "src/legacy/core_plugins/region_map",
+    "regionMap": "src/plugins/region_map",
     "savedObjects": "src/plugins/saved_objects",
     "savedObjectsManagement": "src/plugins/saved_objects_management",
     "server": "src/legacy/server",

docs/apm/images/apm-service-map-anomaly.png

@@ -6,22 +6,31 @@
 <titleabbrev>Integrate with machine learning</titleabbrev>
 ++++
 
-The Machine Learning integration will initiate a new job predefined to calculate anomaly scores on transaction response times.
-The response time graph will show the expected bounds and add an annotation when the anomaly score is 75 or above.
-Jobs can be created per transaction type, and based on the average response time.
-Manage jobs in the *Machine Learning jobs management*.
+The Machine Learning integration initiates a new job predefined to calculate anomaly scores on APM transaction durations.
+Jobs can be created per transaction type, and are based on the service's average response time.
+
+After a machine learning job is created, results are shown in two places:
+
+The transaction duration graph will show the expected bounds and add an annotation when the anomaly score is 75 or above.
+
+[role="screenshot"]
+image::apm/images/apm-ml-integration.png[Example view of anomaly scores on response times in the APM app]
+
+Service maps will display a color-coded anomaly indicator based on the detected anomaly score.
 
 [role="screenshot"]
-image::apm/images/apm-ml-integration.png[Example view of anomaly scores on response times in APM app in Kibana]
+image::apm/images/apm-service-map-anomaly.png[Example view of anomaly scores on service maps in the APM app]
 
 [float]
 [[create-ml-integration]]
 === Create a new machine learning job
 
 To enable machine learning anomaly detection, first choose a service to monitor.
 Then, select **Integrations** > **Enable ML anomaly detection** and click **Create job**.
+
 That's it! After a few minutes, the job will begin calculating results;
 it might take additional time for results to appear on your graph.
+Jobs can be managed in *Machine Learning jobs management*.
 
 APM specific anomaly detection wizards are also available for certain Agents.
 See the machine learning {ml-docs}/ootb-ml-jobs-apm.html[APM anomaly detection configurations] for more information.

docs/apm/images/green-service.png

@@ -9,7 +9,9 @@ Please use Chrome or Firefox if available.
 
 A service map is a real-time visual representation of the instrumented services in your application's architecture.
 It shows you how these services are connected, along with high-level metrics like average transaction duration,
-requests per minute, and errors per minute, that allow you to quickly assess the status of your services.
+requests per minute, and errors per minute.
+If enabled, service maps also integrate with machine learning--for real time health indicators based on anomaly detection scores.
+All of these features can help you to quickly and visually assess the status and health of your services.
 
 We currently surface two types of service maps:
 
@@ -52,6 +54,26 @@ Additional filters are not currently available for service maps.
 [role="screenshot"]
 image::apm/images/service-maps-java.png[Example view of service maps with Java highlighted in the APM app in Kibana]
 
+[float]
+[[service-map-anomaly-detection]]
+=== Anomaly detection with machine learning
+
+Machine learning jobs can be created to calculate anomaly scores on APM transaction durations within the selected service.
+When these jobs are active, service maps will display a color-coded anomaly indicator based on the detected anomaly score:
+
+[horizontal]
+image:apm/images/green-service.png[APM green service]:: Max anomaly score **<=25**. Service is healthy.
+image:apm/images/yellow-service.png[APM yellow service]:: Max anomaly score **26-74**. Anomalous activity detected. Service may be degraded.
+image:apm/images/red-service.png[APM red service]:: Max anomaly score **>=75**. Anomalous activity detected. Service is unhealthy.
+
+[role="screenshot"]
+image::apm/images/apm-service-map-anomaly.png[Example view of anomaly scores on service maps in the APM app]
+
+If an anomaly has been detected, click *view anomalies* to view the anomaly detection metric viewier in the Machine learning app.
+This time series analysis will display additional details on the severity and time of the detected anomalies.
+
+To learn how to create a machine learning job, see <<machine-learning-integration,machine learning integration>>.
+
 [float]
 [[service-maps-legend]]
 === Legend

docs/apm/images/red-service.png

@@ -28,7 +28,6 @@ export declare class IndexPattern implements IIndexPattern
 |  [formatHit](./kibana-plugin-plugins-data-public.indexpattern.formathit.md) |  | <code>any</code> |  |
 |  [id](./kibana-plugin-plugins-data-public.indexpattern.id.md) |  | <code>string</code> |  |
 |  [metaFields](./kibana-plugin-plugins-data-public.indexpattern.metafields.md) |  | <code>string[]</code> |  |
-|  [routes](./kibana-plugin-plugins-data-public.indexpattern.routes.md) |  | <code>{</code><br/><code>        edit: string;</code><br/><code>        addField: string;</code><br/><code>        indexedFields: string;</code><br/><code>        scriptedFields: string;</code><br/><code>        sourceFilters: string;</code><br/><code>    }</code> |  |
 |  [timeFieldName](./kibana-plugin-plugins-data-public.indexpattern.timefieldname.md) |  | <code>string &#124; undefined</code> |  |
 |  [title](./kibana-plugin-plugins-data-public.indexpattern.title.md) |  | <code>string</code> |  |
 |  [type](./kibana-plugin-plugins-data-public.indexpattern.type.md) |  | <code>string</code> |  |