223 lines
6.2 KiB
Plaintext
223 lines
6.2 KiB
Plaintext
[role="xpack"]
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[testenv="platinum"]
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[[ml-get-bucket]]
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=== Get buckets API
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++++
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<titleabbrev>Get buckets</titleabbrev>
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++++
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Retrieves {anomaly-job} results for one or more buckets.
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[[ml-get-bucket-request]]
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==== {api-request-title}
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`GET _ml/anomaly_detectors/<job_id>/results/buckets` +
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`GET _ml/anomaly_detectors/<job_id>/results/buckets/<timestamp>`
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[[ml-get-bucket-prereqs]]
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==== {api-prereq-title}
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* If the {es} {security-features} are enabled, you must have `monitor_ml`,
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`monitor`, `manage_ml`, or `manage` cluster privileges to use this API. You also
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need `read` index privilege on the index that stores the results. The
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`machine_learning_admin` and `machine_learning_user` roles provide these
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privileges. For more information, see
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<<security-privileges>> and
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<<built-in-roles>>.
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[[ml-get-bucket-desc]]
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==== {api-description-title}
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The get buckets API presents a chronological view of the records, grouped by
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bucket.
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[[ml-get-bucket-path-parms]]
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==== {api-path-parms-title}
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`<job_id>`::
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(Required, string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
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`<timestamp>`::
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(Optional, string) The timestamp of a single bucket result. If you do not
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specify this parameter, the API returns information about all buckets.
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[[ml-get-bucket-request-body]]
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==== {api-request-body-title}
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`anomaly_score`::
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(Optional, double) Returns buckets with anomaly scores greater or equal than
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this value.
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`desc`::
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(Optional, boolean)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=desc-results]
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`end`::
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(Optional, string) Returns buckets with timestamps earlier than this time.
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`exclude_interim`::
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(Optional, boolean)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=exclude-interim-results]
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`expand`::
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(Optional, boolean) If true, the output includes anomaly records.
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`page`.`from`::
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(Optional, integer) Skips the specified number of buckets.
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`page`.`size`::
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(Optional, integer) Specifies the maximum number of buckets to obtain.
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`sort`::
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(Optional, string) Specifies the sort field for the requested buckets. By
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default, the buckets are sorted by the `timestamp` field.
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`start`::
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(Optional, string) Returns buckets with timestamps after this time.
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[role="child_attributes"]
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[[ml-get-bucket-results]]
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==== {api-response-body-title}
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The API returns an array of bucket objects, which have the following properties:
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`anomaly_score`::
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(number) The maximum anomaly score, between 0-100, for any of the bucket
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influencers. This is an overall, rate-limited score for the job. All the anomaly
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records in the bucket contribute to this score. This value might be updated as
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new data is analyzed.
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`bucket_influencers`::
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(array) An array of bucket influencer objects.
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+
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.Properties of `bucket_influencers`
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[%collapsible%open]
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====
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`anomaly_score`:::
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(number) A normalized score between 0-100, which is calculated for each bucket
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influencer. This score might be updated as newer data is analyzed.
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`bucket_span`:::
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(number)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=bucket-span-results]
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`initial_anomaly_score`:::
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(number) The score between 0-100 for each bucket influencer. This score is the
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initial value that was calculated at the time the bucket was processed.
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`influencer_field_name`:::
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(string) The field name of the influencer.
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`influencer_field_value`:::
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(string) The field value of the influencer.
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`is_interim`:::
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(boolean)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=is-interim]
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`job_id`:::
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(string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
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`probability`:::
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(number) The probability that the bucket has this behavior, in the range 0 to 1.
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This value can be held to a high precision of over 300 decimal places, so the
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`anomaly_score` is provided as a human-readable and friendly interpretation of
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this.
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`raw_anomaly_score`:::
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(number) Internal.
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`result_type`:::
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(string) Internal. This value is always set to `bucket_influencer`.
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`timestamp`:::
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(date) The start time of the bucket for which these results were calculated.
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====
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`bucket_span`::
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(number)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=bucket-span-results]
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`event_count`::
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(number) The number of input data records processed in this bucket.
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`initial_anomaly_score`::
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(number) The maximum `anomaly_score` for any of the bucket influencers. This is
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the initial value that was calculated at the time the bucket was processed.
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`is_interim`::
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(boolean)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=is-interim]
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`job_id`::
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(string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
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`processing_time_ms`::
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(number) The amount of time, in milliseconds, that it took to analyze the bucket
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contents and calculate results.
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`result_type`::
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(string) Internal. This value is always set to `bucket`.
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`timestamp`::
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(date) The start time of the bucket. This timestamp uniquely identifies the
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bucket.
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+
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--
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NOTE: Events that occur exactly at the timestamp of the bucket are included in
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the results for the bucket.
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--
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[[ml-get-bucket-example]]
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==== {api-examples-title}
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[source,console]
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--------------------------------------------------
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GET _ml/anomaly_detectors/low_request_rate/results/buckets
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{
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"anomaly_score": 80,
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"start": "1454530200001"
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}
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--------------------------------------------------
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// TEST[skip:Kibana sample data]
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In this example, the API returns a single result that matches the specified
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score and time constraints:
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[source,js]
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----
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{
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"count" : 1,
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"buckets" : [
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{
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"job_id" : "low_request_rate",
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"timestamp" : 1578398400000,
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"anomaly_score" : 91.58505459594764,
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"bucket_span" : 3600,
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"initial_anomaly_score" : 91.58505459594764,
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"event_count" : 0,
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"is_interim" : false,
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"bucket_influencers" : [
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{
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"job_id" : "low_request_rate",
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"result_type" : "bucket_influencer",
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"influencer_field_name" : "bucket_time",
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"initial_anomaly_score" : 91.58505459594764,
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"anomaly_score" : 91.58505459594764,
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"raw_anomaly_score" : 0.5758246639716365,
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"probability" : 1.7340849573442696E-4,
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"timestamp" : 1578398400000,
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"bucket_span" : 3600,
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"is_interim" : false
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}
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],
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"processing_time_ms" : 0,
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"result_type" : "bucket"
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}
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]
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}
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---- |