elasticsearch/docs/reference/ml/df-analytics/apis/start-dfanalytics.asciidoc
James Rodewig f56a0f4b66
[DOCS] Remove testenv annotations from doc snippet tests (#80023)
Removes `testenv` annotations and related code. These annotations originally let you skip x-pack snippet tests in the docs. However, that's no longer possible.

Relates to #79309, #31619
2021-11-05 18:38:50 -04:00

95 lines
2.9 KiB
Text

[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"
}
----