Put anomaly detection jobs API
editPut anomaly detection jobs API
editCreates a new anomaly detection job in the cluster. The API accepts a PutJobRequest
object
as a request and returns a PutJobResponse
.
Put anomaly detection jobs request
editA PutJobRequest
requires the following argument:
Job configuration
editThe Job
object contains all the details about the anomaly detection job
configuration.
A Job
requires the following arguments:
Analysis configuration
editThe analysis configuration of the anomaly detection 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:
"analysis_config" : { "bucket_span" : "10m", "detectors" : [ { "detector_description" : "Sum of total", "function" : "sum", "field_name" : "total" } ] }
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:
Detector.Builder detectorBuilder = new Detector.Builder() .setFunction("sum") .setFieldName("total") .setDetectorDescription("Sum of total");
Then the same configuration would be:
Data description
editAfter 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:
"data_description" : { "time_field" : "timestamp" }
Using the DataDescription
object and the high level REST client, the same
configuration would be:
Synchronous execution
editWhen executing a PutJobRequest
in the following manner, the client waits
for the PutJobResponse
to be returned before continuing with code execution:
PutJobResponse response = client.machineLearning().putJob(request, RequestOptions.DEFAULT);
Synchronous calls may throw an IOException
in case of either failing to
parse the REST response in the high-level REST client, the request times out
or similar cases where there is no response coming back from the server.
In cases where the server returns a 4xx
or 5xx
error code, the high-level
client tries to parse the response body error details instead and then throws
a generic ElasticsearchException
and adds the original ResponseException
as a
suppressed exception to it.
Asynchronous execution
editExecuting a PutJobRequest
can also be done in an asynchronous fashion so that
the client can return directly. Users need to specify how the response or
potential failures will be handled by passing the request and a listener to the
asynchronous put-job method:
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. Failure scenarios and expected exceptions are the same as in the
synchronous execution case.
A typical listener for put-job
looks like:
Response
editThe returned PutJobResponse
returns the full representation of
the new machine learning job if it has been successfully created. This will
contain the creation time and other fields initialized using
default values: