Delete anomaly detection jobs API

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Delete anomaly detection jobs API

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Deletes an existing anomaly detection job.

Request

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DELETE _ml/anomaly_detectors/<job_id>

Prerequisites

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Requires the manage_ml cluster privilege. This privilege is included in the machine_learning_admin built-in role.
Before you can delete a job, you must close it (unless you specify the force parameter). See Close jobs.

Description

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All 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

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<job_id>: (Required, string) Identifier for the anomaly detection job.

Query parameters

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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.
delete_user_annotations: (Optional, Boolean) Specifies whether annotations that have been added by the user should be deleted along with any auto-generated annotations when the job is reset. Defaults to false.

Examples

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resp = client.ml.delete_job(
    job_id="total-requests",
)
print(resp)

response = client.ml.delete_job(
  job_id: 'total-requests'
)
puts response

const response = await client.ml.deleteJob({
  job_id: "total-requests",
});
console.log(response);

DELETE _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:

resp = client.ml.delete_job(
    job_id="total-requests",
    wait_for_completion=False,
)
print(resp)

response = client.ml.delete_job(
  job_id: 'total-requests',
  wait_for_completion: false
)
puts response

const response = await client.ml.deleteJob({
  job_id: "total-requests",
  wait_for_completion: "false",
});
console.log(response);

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"
}

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