elasticsearch/docs/reference/ml/anomaly-detection/apis/get-bucket.asciidoc

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

Retrieves {anomaly-job} results for one or more buckets.

[[ml-get-bucket-request]]
==== {api-request-title}

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

`GET _ml/anomaly_detectors/<job_id>/results/buckets/<timestamp>`

[[ml-get-bucket-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. For more information, see
<<security-privileges>> and
<<built-in-roles>>.

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

The get buckets API presents a chronological view of the records, grouped by
bucket.

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

`<job_id>`::
(Required, string)
include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]

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

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

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

`desc`::
(Optional, boolean) If true, the buckets are sorted in descending order.

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

`exclude_interim`::
(Optional, boolean)
include::{docdir}/ml/ml-shared.asciidoc[tag=exclude-interim-results]

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

`page`.`from`::
(Optional, integer) Skips the specified number of buckets.
`page`.`size`::
(Optional, integer) Specifies the maximum number of buckets to obtain.

`sort`::
(Optional, string) Specifies the sort field for the requested buckets. By
default, the buckets are sorted by the `timestamp` field.

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

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

The API returns an array of bucket objects, which have the following properties:

`anomaly_score`::
(number) The maximum anomaly score, between 0-100, for any of the bucket
influencers. This is an overall, rate-limited score for the job. All the anomaly 
records in the bucket contribute to this score. This value might be updated as
new data is analyzed.

`bucket_influencers`::
(array) An array of bucket influencer objects, which have the following
properties:

`bucket_influencers`.`anomaly_score`:::
(number) A normalized score between 0-100, which is calculated for each bucket
influencer. This score might be updated as newer data is analyzed.

`bucket_influencers`.`bucket_span`:::
(number) The length of the bucket in seconds. This value matches the `bucket_span`
that is specified in the job.

`bucket_influencers`.`initial_anomaly_score`:::
(number) The score between 0-100 for each bucket influencer. This score is the
initial value that was calculated at the time the bucket was processed.

`bucket_influencers`.`influencer_field_name`:::
(string) The field name of the influencer.

`bucket_influencers`.`influencer_field_value`:::
(string) The field value of the influencer. 

`bucket_influencers`.`is_interim`:::
(boolean)
include::{docdir}/ml/ml-shared.asciidoc[tag=is-interim]

`bucket_influencers`.`job_id`:::
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]

`bucket_influencers`.`probability`:::
(number) The probability that the bucket has this behavior, in the range 0 to 1.
This value can be held to a high precision of over 300 decimal places, so the
`anomaly_score` is provided as a human-readable and friendly interpretation of
this.

`bucket_influencers`.`raw_anomaly_score`:::
(number) Internal.

`bucket_influencers`.`result_type`:::
(string) Internal. This value is always set to `bucket_influencer`.

`bucket_influencers`.`timestamp`:::
(date) The start time of the bucket for which these results were calculated.

`bucket_span`::
(number)
include::{docdir}/ml/ml-shared.asciidoc[tag=bucket-span-results]

`event_count`::
(number) The number of input data records processed in this bucket.

`initial_anomaly_score`::
(number) The maximum `anomaly_score` for any of the bucket influencers. This is
the initial value that was calculated at the time the bucket was processed.

`is_interim`::
(boolean)
include::{docdir}/ml/ml-shared.asciidoc[tag=is-interim]

`job_id`::
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]

`processing_time_ms`::
(number) The amount of time, in milliseconds, that it took to analyze the
bucket contents and calculate results.

`result_type`::
(string) Internal. This value is always set to `bucket`.

`timestamp`::
(date) The start time of the bucket. This timestamp uniquely identifies the
bucket. 
+
--
NOTE: Events that occur exactly at the timestamp of the bucket are included in
the results for the bucket.

--

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

[source,console]
--------------------------------------------------
GET _ml/anomaly_detectors/low_request_rate/results/buckets
{
  "anomaly_score": 80,
  "start": "1454530200001"
}
--------------------------------------------------
// TEST[skip:Kibana sample data]

In this example, the API returns a single result that matches the specified
score and time constraints:
[source,js]
----
{
  "count" : 1,
  "buckets" : [
    {
      "job_id" : "low_request_rate",
      "timestamp" : 1578398400000,
      "anomaly_score" : 91.58505459594764,
      "bucket_span" : 3600,
      "initial_anomaly_score" : 91.58505459594764,
      "event_count" : 0,
      "is_interim" : false,
      "bucket_influencers" : [
        {
          "job_id" : "low_request_rate",
          "result_type" : "bucket_influencer",
          "influencer_field_name" : "bucket_time",
          "initial_anomaly_score" : 91.58505459594764,
          "anomaly_score" : 91.58505459594764,
          "raw_anomaly_score" : 0.5758246639716365,
          "probability" : 1.7340849573442696E-4,
          "timestamp" : 1578398400000,
          "bucket_span" : 3600,
          "is_interim" : false
        }
      ],
      "processing_time_ms" : 0,
      "result_type" : "bucket"
    }
  ]
}
----