Get anomaly records for an anomaly detection job | Elasticsearch API documentation

Get anomaly records for an anomaly detection job Generally available; Added in 5.4.0

POST /_ml/anomaly_detectors/{job_id}/results/records

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 machine learning 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.

Required authorization

Cluster privileges: monitor_ml

Path parameters

job_id string Required

Identifier for the anomaly detection job.

Query parameters

desc boolean

If true, the results are sorted in descending order.
end string | number

Returns records with timestamps earlier than this time. The default value means results are not limited to specific timestamps.
exclude_interim boolean

If true, the output excludes interim results.
from number

Skips the specified number of records.
record_score number

Returns records with anomaly scores greater or equal than this value.
size number

Specifies the maximum number of records to obtain.
sort string

Specifies the sort field for the requested records.
start string | number

Returns records with timestamps after this time. The default value means results are not limited to specific timestamps.

application/json

Body

desc boolean

Refer to the description for the desc query parameter.
end string | number

A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.

One of:
DateTime string UnitMillis number

Time unit for milliseconds
exclude_interim boolean

Refer to the description for the exclude_interim query parameter.
page object
Hide page attributes Show page attributes object
- from number
  
  Skips the specified number of items.
- size number
  
  Specifies the maximum number of items to obtain.
record_score number

Refer to the description for the record_score query parameter.
sort string

Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
start string | number

A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.

One of:
DateTime string UnitMillis number

Time unit for milliseconds

Responses

200 application/json
Hide response attributes Show response attributes object
- count number Required
- records array[object] Required
  
  Hide records attributes Show records attributes object
  
  actual array[number]
  
  The actual value for the bucket.
  
  anomaly_score_explanation object
  
  Hide anomaly_score_explanation attributes Show anomaly_score_explanation attributes object
  
  anomaly_characteristics_impact number
  
  Impact from the duration and magnitude of the detected anomaly relative to the historical average.
  
  anomaly_length number
  
  Length of the detected anomaly in the number of buckets.
  
  anomaly_type string
  
  Type of the detected anomaly: spike or dip.
  
  high_variance_penalty boolean
  
  Indicates reduction of anomaly score for the bucket with large confidence intervals. If a bucket has large confidence intervals, the score is reduced.
  
  incomplete_bucket_penalty boolean
  
  If the bucket contains fewer samples than expected, the score is reduced.
  
  lower_confidence_bound number
  
  Lower bound of the 95% confidence interval.
  
  multi_bucket_impact number
  
  Impact of the deviation between actual and typical values in the past 12 buckets.
  
  single_bucket_impact number
  
  Impact of the deviation between actual and typical values in the current bucket.
  
  typical_value number
  
  Typical (expected) value for this bucket.
  
  upper_confidence_bound number
  
  Upper bound of the 95% confidence interval.
  
  bucket_span number
  
  Time unit for seconds
  
  by_field_name string
  
  The field used to split the data. In particular, this property is used for analyzing the splits with respect to their own history. It is used for finding unusual values in the context of the split.
  
  by_field_value string
  
  The value of by_field_name.
  
  causes array[object]
  
  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. 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.
  
  Hide causes attributes Show causes attributes object
  
  actual array[number]
  
  by_field_name string
  
  by_field_value string
  
  correlated_by_field_value string
  
  field_name string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  function string
  
  function_description string
  
  geo_results object
  
  Hide geo_results attributes Show geo_results attributes object
  
  actual_point string
  
  The actual value for the bucket formatted as a geo_point.
  
  typical_point string
  
  The typical value for the bucket formatted as a geo_point.
  
  influencers array[object]
  
  over_field_name string
  
  over_field_value string
  
  partition_field_name string
  
  partition_field_value string
  
  probability number Required
  
  typical array[number]
  
  detector_index number Required
  
  A unique identifier for the detector.
  
  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 object
  
  Hide geo_results attributes Show geo_results attributes object
  
  actual_point string
  
  The actual value for the bucket formatted as a geo_point.
  
  typical_point string
  
  The typical value for the bucket formatted as a geo_point.
  
  influencers array[object]
  
  If influencers were specified in the detector configuration, this array contains influencers that contributed to or were to blame for an anomaly.
  
  Hide influencers attributes Show influencers attributes object
  
  influencer_field_name string Required
  
  influencer_field_values array[string] Required
  
  initial_record_score number Required
  
  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 Required
  
  If true, this is an interim result. In other words, the results are calculated based on partial input data.
  
  job_id string Required
  
  Identifier for the anomaly detection job.
  
  over_field_name string
  
  The field used to split the data. In particular, this property is used for analyzing the splits with respect to the history of all splits. It is used for finding unusual values in the population of all splits.
  
  over_field_value string
  
  The value of over_field_name.
  
  partition_field_name string
  
  The field used to segment the analysis. When you use this property, you have completely independent baselines for each value of this field.
  
  partition_field_value string
  
  The value of partition_field_name.
  
  probability number Required
  
  The probability of the individual anomaly occurring, in the range 0 to 1. For example, 0.0000772031. 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.
  
  record_score number Required
  
  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 Required
  
  Internal. This is always set to record.
  
  timestamp number
  
  Time unit for milliseconds
  
  typical array[number]
  
  The typical value for the bucket, according to analytical modeling.

POST /_ml/anomaly_detectors/{job_id}/results/records

GET _ml/anomaly_detectors/low_request_rate/results/records
{
  "sort": "record_score",
  "desc": true,
  "start": "1454944100000"
}

resp = client.ml.get_records(
    job_id="low_request_rate",
    sort="record_score",
    desc=True,
    start="1454944100000",
)

const response = await client.ml.getRecords({
  job_id: "low_request_rate",
  sort: "record_score",
  desc: true,
  start: 1454944100000,
});

response = client.ml.get_records(
  job_id: "low_request_rate",
  body: {
    "sort": "record_score",
    "desc": true,
    "start": "1454944100000"
  }
)

$resp = $client->ml()->getRecords([
    "job_id" => "low_request_rate",
    "body" => [
        "sort" => "record_score",
        "desc" => true,
        "start" => "1454944100000",
    ],
]);

curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"sort":"record_score","desc":true,"start":"1454944100000"}' "$ELASTICSEARCH_URL/_ml/anomaly_detectors/low_request_rate/results/records"

Request example

An example body for a `GET _ml/anomaly_detectors/low_request_rate/results/records` request.

{
  "sort": "record_score",
  "desc": true,
  "start": "1454944100000"
}