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

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

Retrieves anomaly records for an {anomaly-job}.

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

`GET _ml/anomaly_detectors/<job_id>/results/records`

[[ml-get-record-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 <<security-privileges>>, <<built-in-roles>>, and
{ml-docs-setup-privileges}.

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

Records contain the detailed analytical results. They describe the anomalous
activity that has been identified in the input data based on the detector
configuration.

There can be many anomaly records depending on the characteristics and size of
the input data. In practice, there are often too many to be able to manually
process them. The {ml-features} therefore perform a sophisticated aggregation of
the anomaly records into buckets.

The number of record results depends on the number of anomalies found in each
bucket, which relates to the number of time series being modeled and the number
of detectors.

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

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

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

`desc`::
(Optional, Boolean)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=desc-results]

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

`exclude_interim`::
(Optional, Boolean)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=exclude-interim-results]

`page`.`from`::
(Optional, integer) Skips the specified number of records.

`page`.`size`::
(Optional, integer) Specifies the maximum number of records to obtain.

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

`sort`::
(Optional, string) Specifies the sort field for the requested records. By
default, the records are sorted by the `anomaly_score` value.

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

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

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

`actual`::
(array) The actual value for the bucket.

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

`by_field_name`::
(string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=by-field-name]

`by_field_value`::
(string) The value of the by field.

`causes`::
(array) For population analysis, an over field must be specified in the detector.
This property contains an array of anomaly records that are the causes for the
anomaly that has been identified for the over field. If no over fields exist,
this field is not present. This sub-resource contains the most anomalous records
for the `over_field_name`. For scalability reasons, a maximum of the 10 most
significant causes of the anomaly are returned. As part of the core analytical
modeling, these low-level anomaly records are aggregated for their parent over
field record. The causes resource contains similar elements to the record
resource, namely `actual`, `typical`, `geo_results.actual_point`, 
`geo_results.typical_point`, `*_field_name` and `*_field_value`. Probability and
scores are not applicable to causes.

`detector_index`::
(number)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=detector-index]

`field_name`::
(string) Certain functions require a field to operate on, for example, `sum()`.
For those functions, this value is the name of the field to be analyzed.

`function`::
(string) The function in which the anomaly occurs, as specified in the
detector configuration. For example, `max`.

`function_description`::
(string) The description of the function in which the anomaly occurs, as
specified in the detector configuration.

`geo_results.actual_point`::
(string) The actual value for the bucket formatted as a `geo_point`. If the
detector function is `lat_long`, this is a comma delimited string of the
latitude and longitude.

`geo_results.typical_point`::
(string) The typical value for the bucket formatted as a `geo_point`. If the
detector function is `lat_long`, this is a comma delimited string of the
latitude and longitude.

`influencers`::
(array) If `influencers` was specified in the detector configuration, this array 
contains influencers that contributed to or were to blame for an anomaly.

`initial_record_score`::
(number) A normalized score between 0-100, which is based on the probability of
the anomalousness of this record. 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]

`over_field_name`::
(string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=over-field-name]

`over_field_value`::
(string) The value of the over field.

`partition_field_name`::
(string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=partition-field-name]

`partition_field_value`::
(string) The value of the partition field.

`probability`::
(number) The probability of the individual anomaly occurring, in the range `0`
to `1`. This value can be held to a high precision of over 300 decimal places,
so the `record_score` is provided as a human-readable and friendly
interpretation of this.

`multi_bucket_impact`::
(number) an indication of how strongly an anomaly is multi bucket or single
bucket. The value is on a scale of `-5.0` to `+5.0` where `-5.0` means the
anomaly is purely single bucket and `+5.0` means the anomaly is purely multi
bucket.

`record_score`::
(number) A normalized score between 0-100, which is based on the probability of
the anomalousness of this record. Unlike `initial_record_score`, this value will
be updated by a re-normalization process as new data is analyzed.

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

`timestamp`::
(date)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=timestamp-results]

`typical`::
(array) The typical value for the bucket, according to analytical modeling.

NOTE: Additional record properties are added, depending on the fields being
analyzed. For example, if it's analyzing `hostname` as a _by field_, then a field
`hostname` is added to the result document. This information enables you to
filter the anomaly results more easily.

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

[source,console]
--------------------------------------------------
GET _ml/anomaly_detectors/low_request_rate/results/records
{
  "sort": "record_score",
  "desc": true,
  "start": "1454944100000"
}
--------------------------------------------------
// TEST[skip:Kibana sample data]

In this example, the API returns twelve results for the specified
time constraints:
[source,js]
----
{
  "count" : 4,
  "records" : [
    {
      "job_id" : "low_request_rate",
      "result_type" : "record",
      "probability" : 1.3882308899968812E-4,
      "multi_bucket_impact" : -5.0,
      "record_score" : 94.98554565630553,
      "initial_record_score" : 94.98554565630553,
      "bucket_span" : 3600,
      "detector_index" : 0,
      "is_interim" : false,
      "timestamp" : 1577793600000,
      "function" : "low_count",
      "function_description" : "count",
      "typical" : [
        28.254208230188834
      ],
      "actual" : [
        0.0
      ]
    },
  ...
  ]
}
----