Delete anomaly detection jobs API
editDelete anomaly detection jobs API
editDeletes an existing anomaly detection job.
Request
editDELETE _ml/anomaly_detectors/<job_id>
Prerequisites
edit-
Requires the
manage_mlcluster privilege. This privilege is included in themachine_learning_adminbuilt-in role. -
Before you can delete a job, you must close it (unless you specify the
forceparameter). See Close jobs.
Description
editAll job configuration, model state and results are deleted.
Deleting an anomaly detection job must be done via this API only. Do not
delete the job directly from the .ml-* indices using the Elasticsearch delete document
API. When Elasticsearch security features are enabled, make sure no write privileges
are granted to anyone over the .ml-* indices.
It is not currently possible to delete multiple jobs using wildcards or a comma separated list.
If you delete a job that has a datafeed, the request first tries to delete the
datafeed. This behavior is equivalent to calling
delete datafeed with the same timeout and force
parameters as the delete job request.
Path parameters
edit-
<job_id> - (Required, string) Identifier for the anomaly detection job.
Query parameters
edit-
force - (Optional, Boolean) Use to forcefully delete an opened job; this method is quicker than closing and deleting the job.
-
wait_for_completion -
(Optional, boolean) Specifies whether the request should return immediately or
wait until the job deletion completes. Defaults to
true.
Examples
editDELETE _ml/anomaly_detectors/total-requests
When the job is deleted, you receive the following results:
{
"acknowledged": true
}
In the next example we delete the total-requests job asynchronously:
DELETE _ml/anomaly_detectors/total-requests?wait_for_completion=false
When wait_for_completion is set to false, the response contains the id
of the job deletion task:
{
"task": "oTUltX4IQMOUUVeiohTt8A:39"
}