Stopping machine learning

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An orderly shutdown of machine learning ensures that:

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

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

Stopping Datafeeds

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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 _xpack/ml/datafeeds/datafeed-total-requests/_stop

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.

For examples of stopping datafeeds in Kibana, see Managing jobs.

Stopping All Datafeeds

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

POST _xpack/ml/datafeeds/_all/_stop

Closing Jobs

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When you close a 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 jobs. If the datafeed has an end date, the job closes automatically on that end date.

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

POST _xpack/ml/anomaly_detectors/total-requests/_close

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

A job can be opened and closed multiple times throughout its lifecycle.

Closing All Jobs

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

POST _xpack/ml/anomaly_detectors/_all/_close