elasticsearch/docs/reference/ml/df-analytics/apis/put-dfanalytics.asciidoc

[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 <<security-privileges>> and <<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}.
  
  `analyzed_fields.includes`:::
    (Optional, array) An array of strings that defines the fields that will be 
    included in the analysis.
    
  `analyzed_fields.excludes`:::
    (Optional, array) An array of strings that defines the fields that will be 
    excluded from the analysis.

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

`dest`::
  (Required, object) The destination configuration, consisting of `index` and 
  optionally `results_field` (`ml` by default).
  
    `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`.
  
`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`.
  
    `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": {}}`.


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

[[ml-put-dfanalytics-example-od]]
===== {oldetection-cap} example

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

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


The API returns the following result:

[source,console-result]
----
{
    "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/]


[[ml-put-dfanalytics-example-r]]
===== {regression-cap} examples

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

[source,console]
--------------------------------------------------
PUT _ml/data_frame/analytics/house_price_regression_analysis
{
  "source": {
    "index": "houses_sold_last_10_yrs"
  },
  "dest": {
    "index": "house_price_predictions"
  },
  "analysis": 
    {
      "regression": {
        "dependent_variable": "price"
      }
    }
}
--------------------------------------------------
// TEST[skip:TBD]


The API returns the following result:

[source,console-result]
----
{
  "id" : "house_price_regression_analysis",
  "source" : {
    "index" : [
      "houses_sold_last_10_yrs"
    ],
    "query" : {
      "match_all" : { }
    }
  },
  "dest" : {
    "index" : "house_price_predictions",
    "results_field" : "ml"
  },
  "analysis" : {
    "regression" : {
      "dependent_variable" : "price",
      "training_percent" : 100
    }
  },
  "model_memory_limit" : "1gb",
  "create_time" : 1567168659127,
  "version" : "8.0.0"
}
----
// TESTRESPONSE[s/1567168659127/$body.$_path/]
// TESTRESPONSE[s/"version": "8.0.0"/"version": $body.version/]


The following example creates a job and specifies a training percent:

[source,console]
--------------------------------------------------
PUT _ml/data_frame/analytics/student_performance_mathematics_0.3
{
 "source": {
   "index": "student_performance_mathematics"
 },
 "dest": {
   "index":"student_performance_mathematics_reg"
 },
 "analysis":
   {
     "regression": {
       "dependent_variable": "G3",
       "training_percent": 70  <1>
     }
   }
}
--------------------------------------------------
// TEST[skip:TBD]

<1> The `training_percent` defines the percentage of the data set that will be used 
for training the model.