OpenSearch/docs/java-rest/high-level/ml/put-job.asciidoc

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[[java-rest-high-x-pack-ml-put-job]]
=== Put Job API
The Put Job API can be used to create a new {ml} job
in the cluster. The API accepts a `PutJobRequest` object
as a request and returns a `PutJobResponse`.
[[java-rest-high-x-pack-ml-put-job-request]]
==== Put Job Request
A `PutJobRequest` requires the following argument:
["source","java",subs="attributes,callouts,macros"]
--------------------------------------------------
include-tagged::{doc-tests}/MlClientDocumentationIT.java[x-pack-ml-put-job-request]
--------------------------------------------------
<1> The configuration of the {ml} job to create as a `Job`
[[java-rest-high-x-pack-ml-put-job-config]]
==== Job Configuration
The `Job` object contains all the details about the {ml} job
configuration.
A `Job` requires the following arguments:
["source","java",subs="attributes,callouts,macros"]
--------------------------------------------------
include-tagged::{doc-tests}/MlClientDocumentationIT.java[x-pack-ml-put-job-config]
--------------------------------------------------
<1> The job ID
<2> An analysis configuration
<3> A data description
<4> Optionally, a human-readable description
[[java-rest-high-x-pack-ml-put-job-analysis-config]]
==== Analysis Configuration
The analysis configuration of the {ml} job is defined in the `AnalysisConfig`.
`AnalysisConfig` reflects all the configuration
settings that can be defined using the REST API.
Using the REST API, we could define this analysis configuration:
[source,js]
--------------------------------------------------
"analysis_config" : {
"bucket_span" : "10m",
"detectors" : [
{
"detector_description" : "Sum of total",
"function" : "sum",
"field_name" : "total"
}
]
}
--------------------------------------------------
// NOTCONSOLE
Using the `AnalysisConfig` object and the high level REST client, the list
of detectors must be built first.
An example of building a `Detector` instance is as follows:
["source","java",subs="attributes,callouts,macros"]
--------------------------------------------------
include-tagged::{doc-tests}/MlClientDocumentationIT.java[x-pack-ml-put-job-detector]
--------------------------------------------------
<1> The function to use
<2> The field to apply the function to
<3> Optionally, a human-readable description
Then the same configuration would be:
["source","java",subs="attributes,callouts,macros"]
--------------------------------------------------
include-tagged::{doc-tests}/MlClientDocumentationIT.java[x-pack-ml-put-job-analysis-config]
--------------------------------------------------
<1> Create a list of detectors
<2> Pass the list of detectors to the analysis config builder constructor
<3> The bucket span
[[java-rest-high-x-pack-ml-put-job-data-description]]
==== Data Description
After defining the analysis config, the next thing to define is the
data description, using a `DataDescription` instance. `DataDescription`
reflects all the configuration settings that can be defined using the
REST API.
Using the REST API, we could define this metrics configuration:
[source,js]
--------------------------------------------------
"data_description" : {
"time_field" : "timestamp"
}
--------------------------------------------------
// NOTCONSOLE
Using the `DataDescription` object and the high level REST client, the same
configuration would be:
["source","java",subs="attributes,callouts,macros"]
--------------------------------------------------
include-tagged::{doc-tests}/MlClientDocumentationIT.java[x-pack-ml-put-job-data-description]
--------------------------------------------------
<1> The time field
[[java-rest-high-x-pack-ml-put-job-execution]]
==== Execution
The Put Job API can be executed through a `MachineLearningClient`
instance. Such an instance can be retrieved from a `RestHighLevelClient`
using the `machineLearning()` method:
["source","java",subs="attributes,callouts,macros"]
--------------------------------------------------
include-tagged::{doc-tests}/MlClientDocumentationIT.java[x-pack-ml-put-job-execute]
--------------------------------------------------
[[java-rest-high-x-pack-ml-put-job-response]]
==== Response
The returned `PutJobResponse` returns the full representation of
the new {ml} job if it has been successfully created. This will
contain the creation time and other fields initialized using
default values:
["source","java",subs="attributes,callouts,macros"]
--------------------------------------------------
include-tagged::{doc-tests}/MlClientDocumentationIT.java[x-pack-ml-put-job-response]
--------------------------------------------------
<1> The creation time is a field that was not passed in the `Job` object in the request
[[java-rest-high-x-pack-ml-put-job-async]]
==== Asynchronous Execution
This request can be executed asynchronously:
["source","java",subs="attributes,callouts,macros"]
--------------------------------------------------
include-tagged::{doc-tests}/MlClientDocumentationIT.java[x-pack-ml-put-job-execute-async]
--------------------------------------------------
<1> The `PutMlJobRequest` to execute and the `ActionListener` to use when
the execution completes
The asynchronous method does not block and returns immediately. Once it is
completed the `ActionListener` is called back using the `onResponse` method
if the execution successfully completed or using the `onFailure` method if
it failed.
A typical listener for `PutJobResponse` looks like:
["source","java",subs="attributes,callouts,macros"]
--------------------------------------------------
include-tagged::{doc-tests}/MlClientDocumentationIT.java[x-pack-ml-put-job-execute-listener]
--------------------------------------------------
<1> Called when the execution is successfully completed. The response is
provided as an argument
<2> Called in case of failure. The raised exception is provided as an argument