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Reset anomaly detection jobs API

Resets an existing anomaly detection job.

Request

POST _ml/anomaly_detectors/<job_id>/_reset

Prerequisites

Requires the manage_ml cluster privilege. This privilege is included in the machine_learning_admin built-in role.
Before you can reset a job, you must close it. You can set force to true when closing the job to avoid waiting for the job to complete. See Close jobs.

Description

All model state and results are deleted. The job is ready to start over as if it had just been created.

It is not currently possible to reset multiple jobs using wildcards or a comma separated list.

Path parameters

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

Query parameters

wait_for_completion: (Optional, Boolean) Specifies whether the request should return immediately or wait until the job reset 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

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

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

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

POST _ml/anomaly_detectors/total-requests/_reset

When the job is reset, you receive the following results:

{
  "acknowledged": true
}

In the next example we reset the total-requests job asynchronously:

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

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

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

POST _ml/anomaly_detectors/total-requests/_reset?wait_for_completion=false

When wait_for_completion is set to false, the response contains the id of the job reset task:

{
  "task": "oTUltX4IQMOUUVeiohTt8A:39"
}

If you want to check the status of the reset task, use the Task management by referencing the task ID.

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https://www.elastic.co/guide/en/elasticsearch/reference/8.17/ml-reset-job.html