130 lines
3.9 KiB
Plaintext
130 lines
3.9 KiB
Plaintext
--
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:api: put-job
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:request: PutJobRequest
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:response: PutJobResponse
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--
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[role="xpack"]
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[id="{upid}-{api}"]
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=== Put {anomaly-jobs} API
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Creates a new {anomaly-job} in the cluster. The API accepts a +{request}+ object
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as a request and returns a +{response}+.
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[id="{upid}-{api}-request"]
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==== Put {anomaly-jobs} request
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A +{request}+ requires the following argument:
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-request]
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--------------------------------------------------
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<1> The configuration of the {anomaly-job} to create as a `Job`
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[id="{upid}-{api}-config"]
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==== Job configuration
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The `Job` object contains all the details about the {anomaly-job}
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configuration.
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A `Job` requires the following arguments:
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-config]
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--------------------------------------------------
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<1> The job ID
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<2> An analysis configuration
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<3> A data description
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<4> Optionally, a human-readable description
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[id="{upid}-{api}-analysis-config"]
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==== Analysis configuration
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The analysis configuration of the {anomaly-job} is defined in the `AnalysisConfig`.
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`AnalysisConfig` reflects all the configuration
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settings that can be defined using the REST API.
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Using the REST API, we could define this analysis configuration:
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[source,js]
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--------------------------------------------------
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"analysis_config" : {
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"bucket_span" : "10m",
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"detectors" : [
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{
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"detector_description" : "Sum of total",
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"function" : "sum",
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"field_name" : "total"
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}
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]
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}
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--------------------------------------------------
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// NOTCONSOLE
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Using the `AnalysisConfig` object and the high level REST client, the list
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of detectors must be built first.
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An example of building a `Detector` instance is as follows:
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-detector]
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--------------------------------------------------
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<1> The function to use
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<2> The field to apply the function to
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<3> Optionally, a human-readable description
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Then the same configuration would be:
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-analysis-config]
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--------------------------------------------------
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<1> Create a list of detectors
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<2> Pass the list of detectors to the analysis config builder constructor
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<3> The bucket span
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[id="{upid}-{api}-data-description"]
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==== Data description
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After defining the analysis config, the next thing to define is the
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data description, using a `DataDescription` instance. `DataDescription`
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reflects all the configuration settings that can be defined using the
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REST API.
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Using the REST API, we could define this metrics configuration:
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[source,js]
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--------------------------------------------------
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"data_description" : {
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"time_field" : "timestamp"
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}
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--------------------------------------------------
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// NOTCONSOLE
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Using the `DataDescription` object and the high level REST client, the same
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configuration would be:
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-data-description]
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--------------------------------------------------
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<1> The time field
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include::../execution.asciidoc[]
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[id="{upid}-{api}-response"]
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==== Response
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The returned +{response}+ returns the full representation of
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the new {ml} job if it has been successfully created. This will
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contain the creation time and other fields initialized using
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default values:
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-response]
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--------------------------------------------------
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<1> The creation time is a field that was not passed in the `Job` object in the request
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