Stop machine learning anomaly detection
editStop machine learning anomaly detection
editAn 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 datafeeds
editWhen 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
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 datafeeds
editIf you are upgrading your cluster, you can use the following request to stop all datafeeds:
POST _ml/datafeeds/_all/_stop
Closing anomaly detection jobs
editWhen you close an anomaly detection job, it cannot receive data or perform analysis
operations. 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
You must have manage_ml, or manage cluster privileges to stop
anomaly detection jobs. For more information, see
Security privileges.
If you submit a request to close an anomaly detection job and its datafeed is running,
the request first tries to stop the datafeed. This behavior is equivalent to
calling the stop datafeeds API with the same
timeout and force parameters as the close job request.
Anomaly detection jobs can be opened and closed multiple times throughout their lifecycle.
Closing all anomaly detection jobs
editIf 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