---
type: api
title: Get anomaly records for an anomaly detection job
description: **All methods and paths for this operation:**
url: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v9/operation/operation-ml-get-records
resource: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v9/operation/operation-ml-get-records
products:
  - Elasticsearch
---

# Get anomaly records for an anomaly detection job


Availability: 5.4+

## Paths

- `POST` (also `GET`) `/_ml/anomaly_detectors/{job_id}/results/records`

- [Api key auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v9/authentication#apikeyauth)
- [Basic auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v9/authentication#basicauth)
- [Bearer auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v9/authentication#bearerauth)

#### Path Parameters

- `job_id` (string Id)
  Identifier for the anomaly detection job.

## Description

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`

## Query String Parameters

- `desc` (boolean · default: false)
  If true, the results are sorted in descending order.
- `end` (DateTime · default: -1)
  Returns records with timestamps earlier than this time. The default value
means results are not limited to specific timestamps.
- `exclude_interim` (boolean · default: false)
  If `true`, the output excludes interim results.
- `from` (number · default: 0)
  Skips the specified number of records.
- `record_score` (number · default: 0)
  Returns records with anomaly scores greater or equal than this value.
- `size` (number · default: 100)
  Specifies the maximum number of records to obtain.
- `sort` (string Field · default: record_score)
  Specifies the sort field for the requested records.
- `start` (DateTime · default: -1)
  Returns records with timestamps after this time. The default value means
results are not limited to specific timestamps.

## Request Body

`application/json`

- `desc` (boolean · default: false) — optional
  Refer to the description for the `desc` query parameter.
- `end` (DateTime · default: -1) — optional
  Refer to the description for the `end` query parameter.
- `exclude_interim` (boolean · default: false) — optional
  Refer to the description for the `exclude_interim` query parameter.
- `page` ({} Page) — optional
  - `from` (number · default: 0) — optional
    Skips the specified number of items.
  - `size` (number · default: 10000) — optional
    Specifies the maximum number of items to obtain.
- `record_score` (number · default: 0) — optional
  Refer to the description for the `record_score` query parameter.
- `sort` (string Field · default: record_score) — optional
  Refer to the description for the `sort` query parameter.
- `start` (DateTime · default: -1) — optional
  Refer to the description for the `start` query parameter.

## Response

`application/json`

- `count` (number)
- `records` ([] Anomaly)
  - `actual` ([] number)
    The actual value for the bucket.
  - `anomaly_score_explanation` ({} AnomalyExplanation)
    Information about the factors impacting the initial anomaly score.
    - `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` (DurationValueUnitSeconds)
    The length of the bucket in seconds. This value matches the `bucket_span` that is specified in the job.
  - `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` ([] AnomalyCause)
    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.
    - `actual` ([] number)
    - `by_field_name` (string Name)
    - `by_field_value` (string)
    - `correlated_by_field_value` (string)
    - `field_name` (string Field)
    - `function` (string)
    - `function_description` (string)
    - `geo_results` ({} GeoResults)
      - `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` ([] Influence)
      - `influencer_field_name` (string)
      - `influencer_field_values` ([] string)
    - `over_field_name` (string Name)
    - `over_field_value` (string)
    - `partition_field_name` (string)
    - `partition_field_value` (string)
    - `probability` (number)
    - `typical` ([] number)
  - `detector_index` (number)
    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` ({} GeoResults)
    If the detector function is `lat_long`, this object contains comma delimited strings for the latitude and longitude of the actual and typical values.
    - `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` ([] Influence)
    If influencers were specified in the detector configuration, this array contains influencers that contributed to or were to blame for an anomaly.
    - `influencer_field_name` (string)
    - `influencer_field_values` ([] string)
  - `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)
    If true, this is an interim result. In other words, the results are calculated based on partial input data.
  - `job_id` (string)
    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)
    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)
    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` (EpochTimeUnitMillis)
    The start time of the bucket for which these results were calculated.
  - `typical` ([] number)
    The typical value for the bucket, according to analytical modeling.
## Code Examples

#### Console

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

#### JavaScript

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

#### Python

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

#### curl

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

#### Java

```java
client.ml().getRecords(g -> g
    .desc(true)
    .jobId("low_request_rate")
    .sort("record_score")
    .start(DateTime.of("1454944100000"))
);
```

#### C#

```c#
var response = await client.MachineLearning
    .GetRecordsAsync(jobId: "low_request_rate", d1 => d1
        .Desc(true)
        .Sort(x => x.RecordScore)
        .Start(DateTimeOffset.Parse("2016-02-08T15:08:20.0000000+00:00", CultureInfo.InvariantCulture, DateTimeStyles.RoundtripKind))
    );
```

#### PHP

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

#### Ruby

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