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* master: (39 commits) [Canvas][tech-debt] Rename __examples__ to __stories__ (elastic#73853) [Canvas] Storybook Redux Addon (elastic#73227) Use "Apply_filter_trigger" in "explore underlying data" action (elastic#71445) [maps] convert top nav config to TS (elastic#73851) [maps] fix fit to bounds for ES document layers with joins (elastic#73985) [Canvas][tech-debt] Refactor Toolbar (completes Kill Recompose.pure) (elastic#73309) [CI] In-progress Slack notifications (elastic#74012) [SIEM][Detection Engine] Fixes tags to accept characters such as AND, OR, (, ), ", * (elastic#74003) [SECURITY_SOLUTION][ENDPOINT] Fix host list Configuration Status cell link loosing list page/size state (elastic#73989) Tweak injected metadata (elastic#73990) Closes elastic#73998 by using `canAccessML` in the ML capabilities API to (elastic#73999) [SIEM] Fixes toaster errors when siemDefault index is an empty or empty spaces (elastic#73991) [Security Solution] Fix timeline pin event callback (elastic#73981) [Security Solution] Fix unexpected redirect (elastic#73969) [Metrics UI] Fix Metrics Explorer TSVB link to use workaround pattern (elastic#73986) [APM] docs: Update machine learning integration (elastic#73597) [Ingest Manager] Fix limited concurrency helper (elastic#73976) [build/sysv] fix missing env variable rename (elastic#73977) Fix a typo. (elastic#73948) [Ingest Manager] Revert fleet config concurrency rollout to rate limit (elastic#73940) ...
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[role="xpack"] | ||
[[machine-learning-integration]] | ||
=== integration | ||
=== Machine learning integration | ||
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<titleabbrev>Integrate with machine learning</titleabbrev> | ||
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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. | ||
The Machine learning integration initiates a new job predefined to calculate anomaly scores on APM transaction durations. | ||
With this integration, you can quickly pinpoint anomalous transactions and see the health of | ||
any upstream and downstream services. | ||
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After a machine learning job is created, results are shown in two places: | ||
Machine learning jobs are created per environment, and are based on a service's average response time. | ||
Because jobs are created at the environment level, | ||
you can add new services to your existing environments without the need for additional machine learning jobs. | ||
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The transaction duration graph will show the expected bounds and add an annotation when the anomaly score is 75 or above. | ||
After a machine learning job is created, results are shown in two places: | ||
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* The transaction duration chart 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] | ||
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Service maps will display a color-coded anomaly indicator based on the detected anomaly score. | ||
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* Service maps will display a color-coded anomaly indicator based on the detected anomaly score. | ||
+ | ||
[role="screenshot"] | ||
image::apm/images/apm-service-map-anomaly.png[Example view of anomaly scores on service maps in the APM app] | ||
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[float] | ||
[[create-ml-integration]] | ||
=== Create a new machine learning job | ||
=== Enable anomaly detection | ||
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To enable machine learning anomaly detection: | ||
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. From the Services overview, Traces overview, or Service Map tab, | ||
select **Anomaly detection**. | ||
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. Click **Create ML Job**. | ||
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To enable machine learning anomaly detection, first choose a service to monitor. | ||
Then, select **Integrations** > **Enable ML anomaly detection** and click **Create job**. | ||
. Machine learning jobs are created at the environment level. | ||
Select all of the service environments that you want to enable anomaly detection in. | ||
Anomalies will surface for all services and transaction types within the selected environments. | ||
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. Click **Create Jobs**. | ||
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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*. | ||
it might take additional time for results to appear on your service maps. | ||
Existing jobs can be managed in *Machine Learning jobs management*. | ||
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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. | ||
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[float] | ||
[[warning-ml-integration]] | ||
=== Anomaly detection warning | ||
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To make machine learning as easy as possible to set up, | ||
the APM app will warn you when filtered to an environment without a machine learning job. | ||
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[role="screenshot"] | ||
image::apm/images/apm-anomaly-alert.png[Example view of anomaly alert in the APM app] |
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...lopment/core/server/kibana-plugin-core-server.kibanarequestevents.completed_.md
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<!-- Do not edit this file. It is automatically generated by API Documenter. --> | ||
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[Home](./index.md) > [kibana-plugin-core-server](./kibana-plugin-core-server.md) > [KibanaRequestEvents](./kibana-plugin-core-server.kibanarequestevents.md) > [completed$](./kibana-plugin-core-server.kibanarequestevents.completed_.md) | ||
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## KibanaRequestEvents.completed$ property | ||
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Observable that emits once if and when the request has been completely handled. | ||
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<b>Signature:</b> | ||
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```typescript | ||
completed$: Observable<void>; | ||
``` | ||
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## Remarks | ||
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The request may be considered completed if: - A response has been sent to the client; or - The request was aborted. | ||
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