OpenSearch/docs/reference/ml/anomaly-detection/apis/get-overall-buckets.asciidoc

[role="xpack"]
[testenv="platinum"]
[[ml-get-overall-buckets]]
=== Get overall buckets API
++++
<titleabbrev>Get overall buckets</titleabbrev>
++++

Retrieves overall bucket results that summarize the bucket results of multiple
{anomaly-jobs}.

[[ml-get-overall-buckets-request]]
==== {api-request-title}

`GET _ml/anomaly_detectors/<job_id>/results/overall_buckets` +

`GET _ml/anomaly_detectors/<job_id>,<job_id>/results/overall_buckets` +

`GET _ml/anomaly_detectors/_all/results/overall_buckets`

[[ml-get-overall-buckets-prereqs]]
==== {api-prereq-title}

* If the {es} {security-features} are enabled, you must have `monitor_ml`,
`monitor`, `manage_ml`, or `manage` cluster privileges to use this API. You also
need `read` index privilege on the index that stores the results. The
`machine_learning_admin` and `machine_learning_user` roles provide these
privileges. See {stack-ov}/security-privileges.html[Security privileges] and
{stack-ov}/built-in-roles.html[Built-in roles].

[[ml-get-overall-buckets-desc]]
==== {api-description-title}

You can summarize the bucket results for all {anomaly-jobs} by using `_all` or
by specifying `*` as the `<job_id>`.

An overall bucket has a span equal to the largest `bucket_span` value for the
specified {anomaly-jobs}.

The `overall_score` is calculated by combining the scores of all the buckets
within the overall bucket span. First, the maximum `anomaly_score` per
{anomaly-job} in the overall bucket is calculated. Then the `top_n` of those
scores are averaged to result in the `overall_score`. This means that you can
fine-tune the `overall_score` so that it is more or less sensitive to the number
of jobs that detect an anomaly at the same time. For example, if you set `top_n`
to `1`, the `overall_score` is the maximum bucket score in the overall bucket. Alternatively, if you set `top_n` to the number of jobs, the `overall_score` is
high only when all jobs detect anomalies in that overall bucket.

In addition, the optional parameter `bucket_span` may be used in order
to request overall buckets that span longer than the `bucket_span` of the
largest {anomaly-job}. When set, the `overall_score` will be the max
`overall_score` of the corresponding overall buckets with a span equal to the
`bucket_span` of the largest {anomaly-job}.

[[ml-get-overall-buckets-path-parms]]
==== {api-path-parms-title}

`<job_id>`::
  (Required, string) Identifier for the {anomaly-job}. It can be a job
  identifier, a group name, a comma-separated list of jobs or groups, or a
  wildcard expression.

[[ml-get-overall-buckets-request-body]]
==== {api-request-body-title}

`allow_no_jobs`::
  (Optional, boolean) If `false` and the `job_id` does not match any
  {anomaly-jobs}, an error occurs. The default value is `true`.

`bucket_span`::
  (Optional, string) The span of the overall buckets. Must be greater or equal
  to the `bucket_span` of the largest {anomaly-job}. Defaults to the
  `bucket_span` of the largest {anomaly-job}. 

`end`::
  (Optional, string) Returns overall buckets with timestamps earlier than this
  time.

`exclude_interim`::
  (Optional, boolean) If `true`, the output excludes interim overall buckets.
  Overall buckets are interim if any of the job buckets within the overall
  bucket interval are interim. By default, interim results are included.

`overall_score`::
  (Optional, double) Returns overall buckets with overall scores greater or
  equal than this value.

`start`::
  (Optional, string) Returns overall buckets with timestamps after this time.

`top_n`::
  (Optional, integer) The number of top {anomaly-job} bucket scores to be used
  in the `overall_score` calculation. The default value is `1`.

[[ml-get-overall-buckets-results]]
==== {api-response-body-title}

The API returns the following information:

`overall_buckets`::
  (array) An array of overall bucket objects. For more information, see
  <<ml-results-overall-buckets,Overall Buckets>>.

[[ml-get-overall-buckets-example]]
==== {api-examples-title}

The following example gets overall buckets for {anomaly-jobs} with IDs matching
`job-*`:

[source,js]
--------------------------------------------------
GET _ml/anomaly_detectors/job-*/results/overall_buckets
{
  "overall_score": 80,
  "start": "1403532000000"
}
--------------------------------------------------
// CONSOLE
// TEST[skip:todo]

In this example, the API returns a single result that matches the specified
score and time constraints. The `overall_score` is the max job score as
`top_n` defaults to 1 when not specified:
[source,js]
----
{
  "count": 1,
  "overall_buckets": [
    {
      "timestamp" : 1403532000000,
      "bucket_span" : 3600,
      "overall_score" : 80.0,
      "jobs" : [
        {
          "job_id" : "job-1",
          "max_anomaly_score" : 30.0
        },
        {
          "job_id" : "job-2",
          "max_anomaly_score" : 10.0
        },
        {
          "job_id" : "job-3",
          "max_anomaly_score" : 80.0
        }
      ],
      "is_interim" : false,
      "result_type" : "overall_bucket"
    }
  ]
}
----

The next example is similar but this time `top_n` is set to `2`:

[source,js]
--------------------------------------------------
GET _ml/anomaly_detectors/job-*/results/overall_buckets
{
  "top_n": 2,
  "overall_score": 50.0,
  "start": "1403532000000"
}
--------------------------------------------------
// CONSOLE
// TEST[skip:todo]

Note how the `overall_score` is now the average of the top 2 job scores:
[source,js]
----
{
  "count": 1,
  "overall_buckets": [
    {
      "timestamp" : 1403532000000,
      "bucket_span" : 3600,
      "overall_score" : 55.0,
      "jobs" : [
        {
          "job_id" : "job-1",
          "max_anomaly_score" : 30.0
        },
        {
          "job_id" : "job-2",
          "max_anomaly_score" : 10.0
        },
        {
          "job_id" : "job-3",
          "max_anomaly_score" : 80.0
        }
      ],
      "is_interim" : false,
      "result_type" : "overall_bucket"
    }
  ]
}
----