[role="xpack"]
[testenv="platinum"]
[[ml-get-bucket]]
=== Get buckets API
++++
Get buckets
++++
Retrieves {anomaly-job} results for one or more buckets.
[[ml-get-bucket-request]]
==== {api-request-title}
`GET _ml/anomaly_detectors//results/buckets` +
`GET _ml/anomaly_detectors//results/buckets/`
[[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
<> and
<>.
[[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}
``::
(Required, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
``::
(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)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=desc-results]
`end`::
(Optional, string) Returns buckets with timestamps earlier than this time.
`exclude_interim`::
(Optional, boolean)
include::{es-repo-dir}/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.
[role="child_attributes"]
[[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.
+
.Properties of `bucket_influencers`
[%collapsible%open]
====
`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_span`:::
(number)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=bucket-span-results]
`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.
`influencer_field_name`:::
(string) The field name of the influencer.
`influencer_field_value`:::
(string) The field value of the influencer.
`is_interim`:::
(boolean)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=is-interim]
`job_id`:::
(string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
`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.
`raw_anomaly_score`:::
(number) Internal.
`result_type`:::
(string) Internal. This value is always set to `bucket_influencer`.
`timestamp`:::
(date) The start time of the bucket for which these results were calculated.
====
`bucket_span`::
(number)
include::{es-repo-dir}/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::{es-repo-dir}/ml/ml-shared.asciidoc[tag=is-interim]
`job_id`::
(string)
include::{es-repo-dir}/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"
}
]
}
----