Stopping machine learning anomaly detectionedit

An orderly shutdown ensures that:

  • Datafeeds are stopped
  • Buffers are flushed
  • Model history is pruned
  • Final results are calculated
  • Model snapshots are saved
  • Anomaly detection jobs are closed

This process ensures that jobs are in a consistent state in case you want to subsequently re-open them.

Stopping datafeedsedit

When you stop a datafeed, it ceases to retrieve data from Elasticsearch. You can stop a datafeed by using Kibana or the stop datafeeds API. For example, the following request stops the feed1 datafeed:

POST _ml/datafeeds/feed1/_stop
Note

You must have manage_ml, or manage cluster privileges to stop datafeeds. For more information, see Security privileges.

A datafeed can be started and stopped multiple times throughout its lifecycle.

Stopping all datafeedsedit

If you are upgrading your cluster, you can use the following request to stop all datafeeds:

POST _ml/datafeeds/_all/_stop

Closing anomaly detection jobsedit

When you close an anomaly detection job, it cannot receive data or perform analysis operations. If a job is associated with a datafeed, you must stop the datafeed before you can close the job. If the datafeed has an end date, the job closes automatically on that end date.

You can close a job by using the close anomaly detection job API. For example, the following request closes the job1 job:

POST _ml/anomaly_detectors/job1/_close
Note

You must have manage_ml, or manage cluster privileges to stop datafeeds. For more information, see Security privileges.

Anomaly detection jobs can be opened and closed multiple times throughout their lifecycle.

Closing all anomaly detection jobsedit

If you are upgrading your cluster, you can use the following request to close all open anomaly detection jobs on the cluster:

POST _ml/anomaly_detectors/_all/_close