Open anomaly detection jobs API

An OpenJobRequest object gets created with an existing non-null jobId.

OpenJobRequest openJobRequest = new OpenJobRequest("opening-my-first-machine-learning-job"); 
openJobRequest.setTimeout(TimeValue.timeValueMinutes(10)); 

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

OpenJobResponse openJobResponse = client.machineLearning().openJob(openJobRequest, 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 OpenJobRequest 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 open-job method:

client.machineLearning().openJobAsync(openJobRequest, 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 open-job looks like:

ActionListener<OpenJobResponse> listener = new ActionListener<OpenJobResponse>() {
    @Override
    public void onResponse(OpenJobResponse openJobResponse) {
        
    }

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