[role="xpack"]
[testenv="platinum"]
[[put-dfanalytics]]
=== Create {dfanalytics-jobs} API
[subs="attributes"]
++++
<titleabbrev>Create {dfanalytics-jobs}</titleabbrev>
++++

Instantiates a {dfanalytics-job}.

experimental[]

[[ml-put-dfanalytics-request]]
==== {api-request-title}

`PUT _ml/data_frame/analytics/<data_frame_analytics_id>`

[[ml-put-dfanalytics-prereq]]
==== {api-prereq-title}

* You must have `machine_learning_admin` built-in role to use this API. You must 
also have `read` and `view_index_metadata` privileges on the source index and 
`read`, `create_index`, and `index` privileges on the destination index. For 
more information, see {stack-ov}/security-privileges.html[Security privileges] 
and {stack-ov}/built-in-roles.html[Built-in roles].

[[ml-put-dfanalytics-desc]]
==== {api-description-title}

This API creates a {dfanalytics-job} that performs an analysis on the source 
index and stores the outcome in a destination index.

The destination index will be automatically created if it does not exist. The 
`index.number_of_shards` and `index.number_of_replicas` settings of the source 
index will be copied over the destination index. When the source index matches 
multiple indices, these settings will be set to the maximum values found in the 
source indices.

The mappings of the source indices are also attempted to be copied over
to the destination index, however, if the mappings of any of the fields don't 
match among the source indices, the attempt will fail with an error message.

If the destination index already exists, then it will be use as is. This makes 
it possible to set up the destination index in advance with custom settings 
and mappings.

[[ml-put-dfanalytics-path-params]]
==== {api-path-parms-title}

`<data_frame_analytics_id>`::
  (Required, string) A numerical character string that uniquely identifies 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.

[[ml-put-dfanalytics-request-body]]
==== {api-request-body-title}

`analysis`::
  (Required, object) Defines the type of {dfanalytics} you want to perform on your source 
  index. For example: `outlier_detection`. See <<dfanalytics-types>>.
  
`analyzed_fields`::
  (Optional, 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}.

`description`::
  (Optional, string) A description of the job.

`dest`::
  (Required, object) The destination configuration, consisting of `index` and 
  optionally `results_field` (`ml` by default). See 
  <<ml-dfanalytics-properties,{dfanalytics} properties>>.
  
`model_memory_limit`::
  (Optional, 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`::
  (Required, object) The source configuration, consisting of `index` and 
  optionally a `query`. See 
  <<ml-dfanalytics-properties,{dfanalytics} properties>>. 

[[ml-put-dfanalytics-example]]
==== {api-examples-title}

The following example creates the `loganalytics` {dfanalytics-job}, the analysis 
type is `outlier_detection`:

[source,js]
--------------------------------------------------
PUT _ml/data_frame/analytics/loganalytics
{
  "description": "Outlier detection on log data",
  "source": {
    "index": "logdata"
  },
  "dest": {
    "index": "logdata_out"
  },
  "analysis": {
    "outlier_detection": {
    }
  }
}
--------------------------------------------------
// CONSOLE
// TEST[setup:setup_logdata]

The API returns the following result:

[source,js]
----
{
    "id": "loganalytics",
    "description": "Outlier detection on log data",
    "source": {
        "index": ["logdata"],
        "query": {
            "match_all": {}
        }
    },
    "dest": {
        "index": "logdata_out",
        "results_field": "ml"
    },
    "analysis": {
        "outlier_detection": {}
    },
    "model_memory_limit": "1gb",
    "create_time" : 1562265491319,
    "version" : "8.0.0"
}
----
// TESTRESPONSE[s/1562265491319/$body.$_path/]
// TESTRESPONSE[s/"version": "8.0.0"/"version": $body.version/]