Update Job API

An UpdateJobRequest object gets created with a JobUpdate object.

UpdateJobRequest updateJobRequest = new UpdateJobRequest(update); 

The JobUpdate object has many optional arguments with which to update an existing machine learning job. An existing, non-null jobId must be referenced in its creation.

JobUpdate update = new JobUpdate.Builder(jobId) 
    .setDescription("My description") 
    .setAnalysisLimits(new AnalysisLimits(1000L, null)) 
    .setBackgroundPersistInterval(TimeValue.timeValueHours(3)) 
    .setCategorizationFilters(Arrays.asList("categorization-filter")) 
    .setDetectorUpdates(Arrays.asList(detectorUpdate)) 
    .setGroups(Arrays.asList("job-group-1")) 
    .setResultsRetentionDays(10L) 
    .setModelPlotConfig(new ModelPlotConfig(true, null)) 
    .setModelSnapshotRetentionDays(7L) 
    .setCustomSettings(customSettings) 
    .setRenormalizationWindowDays(3L) 
    .build();

Included with these options are specific optional JobUpdate.DetectorUpdate updates.

JobUpdate.DetectorUpdate detectorUpdate = new JobUpdate.DetectorUpdate(0, 
    "detector description", 
    detectionRules); 

When executing a UpdateJobRequest in the following manner, the client waits for the PutJobResponse to be returned before continuing with code execution:

PutJobResponse updateJobResponse = client.machineLearning().updateJob(updateJobRequest, 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.

Executing a UpdateJobRequest 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 update-job method:

client.machineLearning().updateJobAsync(updateJobRequest, RequestOptions.DEFAULT, listener); 

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 update-job looks like:

ActionListener<PutJobResponse> listener = new ActionListener<PutJobResponse>() {
    @Override
    public void onResponse(PutJobResponse updateJobResponse) {
        
    }

    @Override
    public void onFailure(Exception e) {
        
    }
};

A PutJobResponse contains the updated Job object

Job updatedJob = updateJobResponse.getResponse();