OpenSearch/docs/en/rest-api/ml/get-bucket.asciidoc

//lcawley Verified example output 2017-04-11
[[ml-get-bucket]]
==== Get Buckets

The get bucket API enables you to retrieve information about buckets in the
results from a job.


===== Request

`GET _xpack/ml/anomaly_detectors/<job_id>/results/buckets` +

`GET _xpack/ml/anomaly_detectors/<job_id>/results/buckets/<timestamp>`


===== Description

This API presents a chronological view of the records, grouped by bucket.


===== Path Parameters

`job_id`::
  (string) Identifier for the job

`timestamp`::
  (string) The timestamp of a single bucket result.
  If you do not specify this optional parameter, the API returns information
  about all buckets.


===== Request Body

`anomaly_score`::
  (double) Returns buckets with anomaly scores higher than this value.

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

`exclude_interim`::
  (boolean) If true, the output excludes interim results.
  By default, interim results are included.

`expand`::
  (boolean) If true, the output includes anomaly records.

`from`::
  (integer) Skips the specified number of buckets.

`size`::
  (integer) Specifies the maximum number of buckets to obtain.

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


===== Results

The API returns the following information:

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


===== Authorization

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. For more information, see
<<security-privileges>> and <<built-in-roles>>.


===== Examples

The following example gets bucket information for the `it-ops-kpi` job:

[source,js]
--------------------------------------------------
GET _xpack/ml/anomaly_detectors/it-ops-kpi/results/buckets
{
  "anomaly_score": 80,
  "start": "1454530200001"
}
--------------------------------------------------
// CONSOLE
// TEST[skip:todo]

In this example, the API returns a single result that matches the specified
score and time constraints:
[source,js]
----
{
  "count": 1,
  "buckets": [
    {
      "job_id": "it-ops-kpi",
      "timestamp": 1454943900000,
      "anomaly_score": 94.1706,
      "bucket_span": 300,
      "initial_anomaly_score": 94.1706,
      "record_count": 1,
      "event_count": 153,
      "is_interim": false,
      "bucket_influencers": [
        {
          "job_id": "it-ops-kpi",
          "result_type": "bucket_influencer",
          "influencer_field_name": "bucket_time",
          "initial_anomaly_score": 94.1706,
          "anomaly_score": 94.1706,
          "raw_anomaly_score": 2.32119,
          "probability": 0.00000575042,
          "timestamp": 1454943900000,
          "bucket_span": 300,
          "sequence_num": 2,
          "is_interim": false
        }
      ],
      "processing_time_ms": 2,
      "partition_scores": [],
      "result_type": "bucket"
    }
  ]
}
----