Get anomaly detection job model snapshot upgrade statistics API

Get anomaly detection job model snapshot upgrade statistics API

New API reference

For the most up-to-date API details, refer to Machine learning anomaly detection APIs.

Retrieves usage information for anomaly detection job model snapshot upgrades.

Request

GET _ml/anomaly_detectors/<job_id>/model_snapshots/<snapshot_id>/_upgrade/_stats

GET _ml/anomaly_detectors/<job_id>,<job_id>/model_snapshots/_all/_upgrade/_stats

GET _ml/anomaly_detectors/_all/model_snapshots/_all/_upgrade/_stats

Prerequisites

Requires the monitor_ml cluster privilege. This privilege is included in the machine_learning_user built-in role.

Description

Anomaly detection job model snapshot upgrades are ephemeral. Only upgrades that are in progress at the time this API is called will be returned.

Path parameters

<job_id>

(string) Identifier for the anomaly detection job. It can be a job identifier, a group name, or a wildcard expression.

<snapshot_id>

(string) Identifier for the model snapshot.

You can get statistics for multiple anomaly detection job model snapshot upgrades in a single API request by using a comma-separated list of snapshot IDs. You can also use wildcard expressions or _all.

Query parameters

allow_no_match

(Optional, Boolean) Specifies what to do when the request:

• Contains wildcard expressions and there are no jobs that match.
• Contains the _all string or no identifiers and there are no matches.
• Contains wildcard expressions and there are only partial matches.

The default value is true, which returns an empty jobs array when there are no matches and the subset of results when there are partial matches. If this parameter is false, the request returns a 404 status code when there are no matches or only partial matches.

Response body

The API returns an array of anomaly detection job model snapshot upgrade status objects. All of these properties are informational; you cannot update their values.

assignment_explanation

(string) For started datafeeds only, contains messages relating to the selection of a node.

job_id

(string) Identifier for the anomaly detection job.

node

(object) Contains properties for the node that runs the upgrade task. This information is available only for upgrade tasks that are assigned to a node.

Details

• attributes

  (object) Lists node attributes such as ml.machine_memory or ml.max_open_jobs settings.

  ephemeral_id

  (string) The ephemeral ID of the node.

  id

  (string) The unique identifier of the node.

  name

  (string) The node name. For example, 0-o0tOo.

  transport_address

  (string) The host and port where transport HTTP connections are accepted.

snapshot_id

(string) A numerical character string that uniquely identifies the model snapshot. For example, 1575402236000.

state

(string) One of loading_old_state, saving_new_state, stopped or failed.

Response codes

404 (Missing resources)

If allow_no_match is false, this code indicates that there are no resources that match the request or only partial matches for the request.

Examples

resp = client.ml.get_model_snapshot_upgrade_stats(
    job_id="low_request_rate",
    snapshot_id="_all",
)
print(resp)

response = client.ml.get_model_snapshot_upgrade_stats(
  job_id: 'low_request_rate',
  snapshot_id: '_all'
)
puts response

const response = await client.ml.getModelSnapshotUpgradeStats({
  job_id: "low_request_rate",
  snapshot_id: "_all",
});
console.log(response);

GET _ml/anomaly_detectors/low_request_rate/model_snapshots/_all/_upgrade/_stats

The API returns the following results:

{
  "count" : 1,
  "model_snapshot_upgrades" : [
    {
      "job_id" : "low_request_rate",
      "snapshot_id" : "1828371",
      "state" : "saving_new_state",
      "node" : {
        "id" : "7bmMXyWCRs-TuPfGJJ_yMw",
        "name" : "node-0",
        "ephemeral_id" : "hoXMLZB0RWKfR9UPPUCxXX",
        "transport_address" : "127.0.0.1:9300",
        "attributes" : {
          "ml.machine_memory" : "17179869184",
          "ml.max_open_jobs" : "512"
        }
      },
      "assignment_explanation" : ""
    }
  ]
}