OpenSearch/docs/reference/ml/anomaly-detection/functions/rare.asciidoc

[role="xpack"]
[[ml-rare-functions]]
=== Rare functions

The rare functions detect values that occur rarely in time or rarely for a
population.

The `rare` analysis detects anomalies according to the number of distinct rare
values. This differs from `freq_rare`, which detects anomalies according to the
number of times (frequency) rare values occur.

[NOTE]
====
* The `rare` and `freq_rare` functions should not be used in conjunction with
`exclude_frequent`.
* You cannot create forecasts for {anomaly-jobs} that contain `rare` or
`freq_rare` functions. 
* You cannot add rules with conditions to detectors that use `rare` or 
`freq_rare` functions. 
* Shorter bucket spans (less than 1 hour, for example) are recommended when
looking for rare events. The functions model whether something happens in a
bucket at least once. With longer bucket spans, it is more likely that
entities will be seen in a bucket and therefore they appear less rare.
Picking the ideal the bucket span depends on the characteristics of the data
with shorter bucket spans typically being measured in minutes, not hours.
* To model rare data, a learning period of at least 20 buckets is required
for typical data.
====

The {ml-features} include the following rare functions:

* <<ml-rare,`rare`>>
* <<ml-freq-rare,`freq_rare`>>


[float]
[[ml-rare]]
==== Rare

The `rare` function detects values that occur rarely in time or rarely for a
population. It detects anomalies according to the number of distinct rare values.

This function supports the following properties:

* `by_field_name` (required)
* `over_field_name` (optional)
* `partition_field_name` (optional)

For more information about those properties, see
{ref}/ml-job-resource.html#ml-detectorconfig[Detector configuration objects].

.Example 1: Analyzing status codes with the rare function
[source,js]
--------------------------------------------------
{
  "function" : "rare",
  "by_field_name" : "status"
}
--------------------------------------------------
// NOTCONSOLE

If you use this `rare` function in a detector in your {anomaly-job}, it detects
values that are rare in time. It models status codes that occur over time and
detects when rare status codes occur compared to the past. For example, you can
detect status codes in a web access log that have never (or rarely) occurred
before.

.Example 2: Analyzing status codes in a population with the rare function
[source,js]
--------------------------------------------------
{
  "function" : "rare",
  "by_field_name" : "status",
  "over_field_name" : "clientip"
}
--------------------------------------------------
// NOTCONSOLE

If you use this `rare` function in a detector in your {anomaly-job}, it detects
values that are rare in a population. It models status code and client IP
interactions that occur. It defines a rare status code as one that occurs for
few client IP values compared to the population. It detects client IP values
that experience one or more distinct rare status codes compared to the
population. For example in a web access log, a `clientip` that experiences the
highest number of different rare status codes compared to the population is
regarded as highly anomalous. This analysis is based on the number of different
status code values, not the count of occurrences.

NOTE: To define a status code as rare the {ml-features} look at the number
of distinct status codes that occur, not the number of times the status code
occurs. If a single client IP experiences a single unique status code, this
is rare, even if it occurs for that client IP in every bucket.

[float]
[[ml-freq-rare]]
==== Freq_rare

The `freq_rare` function detects values that occur rarely for a population.
It detects anomalies according to the number of times (frequency) that rare
values occur.

This function supports the following properties:

* `by_field_name` (required)
* `over_field_name` (required)
* `partition_field_name` (optional)

For more information about those properties, see
{ref}/ml-job-resource.html#ml-detectorconfig[Detector configuration objects].

.Example 3: Analyzing URI values in a population with the freq_rare function
[source,js]
--------------------------------------------------
{
  "function" : "freq_rare",
  "by_field_name" : "uri",
  "over_field_name" : "clientip"
}
--------------------------------------------------
// NOTCONSOLE

If you use this `freq_rare` function in a detector in your {anomaly-job}, it
detects values that are frequently rare in a population. It models URI paths and
client IP interactions that occur. It defines a rare URI path as one that is
visited by few client IP values compared to the population. It detects the
client IP values that experience many interactions with rare URI paths compared
to the population. For example in a web access log, a client IP that visits
one or more rare URI paths many times compared to the population is regarded as
highly anomalous. This analysis is based on the count of interactions with rare
URI paths, not the number of different URI path values.

NOTE: To define a URI path as rare, the analytics consider the number of
distinct values that occur and not the number of times the URI path occurs.
If a single client IP visits a single unique URI path, this is rare, even if it
occurs for that client IP in every bucket.