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. 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
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 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