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

[role="xpack"]
[[start-dfanalytics]]
= Start {dfanalytics-jobs} API

[subs="attributes"]
++++
<titleabbrev>Start {dfanalytics-jobs}</titleabbrev>
++++

Starts a {dfanalytics-job}.


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

`POST _ml/data_frame/analytics/<data_frame_analytics_id>/_start`

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

Requires the following privileges:

* cluster: `manage_ml` (the `machine_learning_admin` built-in role grants this 
  privilege)
* source indices: `read`, `view_index_metadata`
* destination index: `read`, `create_index`, `manage` and `index`


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

A {dfanalytics-job} can be started and stopped multiple times throughout its 
lifecycle.

If the destination index does not exist, it is created automatically the first
time you start the {dfanalytics-job}. The `index.number_of_shards` and
`index.number_of_replicas` settings for the destination index are copied from
the source index. If there are multiple source indices, the destination index
copies the highest setting values. The mappings for the destination index are
also copied from the source indices. If there are any mapping conflicts, the job
fails to start.

If the destination index exists, it is used as is. You can therefore set up the
destination index in advance with custom settings and mappings.

IMPORTANT: When {es} {security-features} are enabled, the {dfanalytics-job}
remembers which user created it and runs the job using those credentials. If you 
provided <<http-clients-secondary-authorization,secondary authorization headers>>
when you created the job, those credentials are used.

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

`<data_frame_analytics_id>`::
(Required, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=job-id-data-frame-analytics-define]

[[ml-start-dfanalytics-query-params]]
== {api-query-parms-title}

`timeout`::
(Optional, <<time-units,time units>>) 
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=timeout-start]

[[ml-start-dfanalytics-response-body]]
== {api-response-body-title}

`acknowledged`::
  (Boolean) For a successful response, this value is always `true`. On failure, an
  exception is returned instead.

`node`::
  (string) The ID of the node that the job was started on.
  If the job is allowed to open lazily and has not yet been assigned to a node, this value is an empty string.

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

The following example starts the `loganalytics` {dfanalytics-job}:

[source,console]
--------------------------------------------------
POST _ml/data_frame/analytics/loganalytics/_start
--------------------------------------------------
// TEST[skip:setup:logdata_job]

When the {dfanalytics-job} starts, you receive the following results:

[source,console-result]
----
{
  "acknowledged" : true,
  "node" : "node-1"
}
----