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

Path parameters

job_idstring Required
Identifier for the anomaly detection job.

Query parameters

descboolean
If true, the results are sorted in descending order.
endstring | number
Returns records with timestamps earlier than this time. The default value means results are not limited to specific timestamps.
exclude_interimboolean
If true, the output excludes interim results.
fromnumber
Skips the specified number of records.
record_scorenumber
Returns records with anomaly scores greater or equal than this value.
sizenumber
Specifies the maximum number of records to obtain.
sortstring
Specifies the sort field for the requested records.
startstring | number
Returns records with timestamps after this time. The default value means results are not limited to specific timestamps.

application/json

Body

descboolean
Refer to the description for the desc query parameter.
endstring | 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:
DateTimestring UnitMillisnumber
Time unit for milliseconds
exclude_interimboolean
Refer to the description for the exclude_interim query parameter.
pageobject
Hide page attributes Show page attributes object
- fromnumber
  Skips the specified number of items.
- sizenumber
  Specifies the maximum number of items to obtain.
record_scorenumber
Refer to the description for the record_score query parameter.
sortstring
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
startstring | 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:
DateTimestring UnitMillisnumber
Time unit for milliseconds

Responses

200 application/json
Hide response attributes Show response attributes object
- countnumber Required
- recordsarray[object] Required
  Hide records attributes Show records attributes object
  actualarray[number]
  The actual value for the bucket.
  anomaly_score_explanationobject
  Hide anomaly_score_explanation attributes Show anomaly_score_explanation attributes object
  anomaly_characteristics_impactnumber
  Impact from the duration and magnitude of the detected anomaly relative to the historical average.
  anomaly_lengthnumber
  Length of the detected anomaly in the number of buckets.
  anomaly_typestring
  Type of the detected anomaly: spike or dip.
  high_variance_penaltyboolean
  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_penaltyboolean
  If the bucket contains fewer samples than expected, the score is reduced.
  lower_confidence_boundnumber
  Lower bound of the 95% confidence interval.
  multi_bucket_impactnumber
  Impact of the deviation between actual and typical values in the past 12 buckets.
  single_bucket_impactnumber
  Impact of the deviation between actual and typical values in the current bucket.
  typical_valuenumber
  Typical (expected) value for this bucket.
  upper_confidence_boundnumber
  Upper bound of the 95% confidence interval.
  bucket_spannumber
  Time unit for seconds
  by_field_namestring
  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_valuestring
  The value of by_field_name.
  causesarray[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
  actualarray[number]
  by_field_namestring
  by_field_valuestring
  correlated_by_field_valuestring
  field_namestring
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  functionstring
  function_descriptionstring
  geo_resultsobject
  Hide geo_results attributes Show geo_results attributes object
  actual_pointstring
  The actual value for the bucket formatted as a geo_point.
  typical_pointstring
  The typical value for the bucket formatted as a geo_point.
  influencersarray[object]
  over_field_namestring
  over_field_valuestring
  partition_field_namestring
  partition_field_valuestring
  probabilitynumber Required
  typicalarray[number]
  detector_indexnumber Required
  A unique identifier for the detector.
  field_namestring
  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.
  functionstring
  The function in which the anomaly occurs, as specified in the detector configuration. For example, max.
  function_descriptionstring
  The description of the function in which the anomaly occurs, as specified in the detector configuration.
  geo_resultsobject
  Hide geo_results attributes Show geo_results attributes object
  actual_pointstring
  The actual value for the bucket formatted as a geo_point.
  typical_pointstring
  The typical value for the bucket formatted as a geo_point.
  influencersarray[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_namestring Required
  influencer_field_valuesarray[string] Required
  initial_record_scorenumber 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_interimboolean Required
  If true, this is an interim result. In other words, the results are calculated based on partial input data.
  job_idstring Required
  Identifier for the anomaly detection job.
  over_field_namestring
  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_valuestring
  The value of over_field_name.
  partition_field_namestring
  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_valuestring
  The value of partition_field_name.
  probabilitynumber 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_scorenumber 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_typestring Required
  Internal. This is always set to record.
  timestampnumber
  Time unit for milliseconds
  typicalarray[number]
  The typical value for the bucket, according to analytical modeling.

GET /_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"
}

Path parameters

Query parameters

Body

endstring | number

startstring | number

Responses