OpenSearch/docs/reference/ml/df-analytics/apis/dfanalyticsresources.asciidoc

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[role="xpack"]
[testenv="platinum"]
[[ml-dfanalytics-resources]]
=== {dfanalytics-cap} job resources
{dfanalytics-cap} resources relate to APIs such as <<put-dfanalytics>> and
<<get-dfanalytics>>.
[discrete]
[[ml-dfanalytics-properties]]
==== {api-definitions-title}
`analysis`::
(object) The type of analysis that is performed on the `source`. For example:
`outlier_detection`. For more information, see <<dfanalytics-types>>.
`analyzed_fields`::
(object) You can specify both `includes` and/or `excludes` patterns. If
`analyzed_fields` is not set, only the relevant fields will be included. For
example all the numeric fields for {oldetection}.
`analyzed_fields.includes`:::
(array) An array of strings that defines the fields that will be included in
the analysis.
`analyzed_fields.excludes`:::
(array) An array of strings that defines the fields that will be excluded
from the analysis.
[source,js]
--------------------------------------------------
PUT _ml/data_frame/analytics/loganalytics
{
"source": {
"index": "logdata"
},
"dest": {
"index": "logdata_out"
},
"analysis": {
"outlier_detection": {
}
},
"analyzed_fields": {
"includes": [ "request.bytes", "response.counts.error" ],
"excludes": [ "source.geo" ]
}
}
--------------------------------------------------
// CONSOLE
// TEST[setup:setup_logdata]
`description`::
(Optional, string) A description of the job.
`dest`::
(object) The destination configuration of the analysis.
`index`:::
(Required, string) Defines the _destination index_ to store the results of
the {dfanalytics-job}.
`results_field`:::
(Optional, string) Defines the name of the field in which to store the
results of the analysis. Default to `ml`.
`id`::
(string) The unique identifier for the {dfanalytics-job}. This identifier can
contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and
underscores. It must start and end with alphanumeric characters. This property
is informational; you cannot change the identifier for existing jobs.
`model_memory_limit`::
(string) The approximate maximum amount of memory resources that are
permitted for analytical processing. The default value for {dfanalytics-jobs}
is `1gb`. If your `elasticsearch.yml` file contains an
`xpack.ml.max_model_memory_limit` setting, an error occurs when you try to
create {dfanalytics-jobs} that have `model_memory_limit` values greater than
that setting. For more information, see <<ml-settings>>.
`source`::
(object) The source configuration consisting an `index` and optionally a
`query` object.
`index`:::
(Required, string or array) Index or indices on which to perform the
analysis. It can be a single index or index pattern as well as an array of
indices or patterns.
`query`:::
(Optional, object) The {es} query domain-specific language
(<<query-dsl,DSL>>). This value corresponds to the query object in an {es}
search POST body. All the options that are supported by {es} can be used,
as this object is passed verbatim to {es}. By default, this property has
the following value: `{"match_all": {}}`.
[[dfanalytics-types]]
==== Analysis objects
{dfanalytics-cap} resources contain `analysis` objects. For example, when you
create a {dfanalytics-job}, you must define the type of analysis it performs.
Currently, `outlier_detection` is the only available type of analysis, however,
other types will be added, for example `regression`.
[discrete]
[[oldetection-resources]]
==== {oldetection-cap} configuration objects
An {oldetection} configuration object has the following properties:
`n_neighbors`::
(integer) Defines the value for how many nearest neighbors each method of
{oldetection} will use to calculate its {olscore}. When the value is
not set, the system will dynamically detect an appropriate value.
`method`::
(string) Sets the method that {oldetection} uses. If the method is not set
{oldetection} uses an ensemble of different methods and normalises and
combines their individual {olscores} to obtain the overall {olscore}. We
recommend to use the ensemble method. Available methods are `lof`, `ldof`,
`distance_kth_nn`, `distance_knn`.
`feature_influence_threshold`::
(double) The minimum {olscore} that a document needs to have in order to
calculate its {fiscore}.
Value range: 0-1 (`0.1` by default).