Put data frame analytics jobs API

A PutDataFrameAnalyticsRequest requires the following argument:

PutDataFrameAnalyticsRequest request = new PutDataFrameAnalyticsRequest(config); 

The DataFrameAnalyticsConfig object contains all the details about the data frame analytics job configuration and contains the following arguments:

DataFrameAnalyticsConfig config = DataFrameAnalyticsConfig.builder("my-analytics-config") 
    .setSource(sourceConfig) 
    .setDest(destConfig) 
    .setAnalysis(outlierDetection) 
    .setAnalyzedFields(analyzedFields) 
    .setModelMemoryLimit(new ByteSizeValue(5, ByteSizeUnit.MB)) 
    .build();

The index and the query from which to collect data.

DataFrameAnalyticsSource sourceConfig = DataFrameAnalyticsSource.builder() 
    .setIndex("put-test-source-index") 
    .setQueryConfig(queryConfig) 
    .build();

QueryConfig queryConfig = new QueryConfig(new MatchAllQueryBuilder());

The index to which data should be written by the data frame analytics job.

DataFrameAnalyticsDest destConfig = DataFrameAnalyticsDest.builder() 
    .setIndex("put-test-dest-index") 
    .build();

The analysis to be performed. Currently, only one analysis is supported: OutlierDetection.

OutlierDetection analysis can be created in one of two ways:

DataFrameAnalysis outlierDetection = OutlierDetection.createDefault(); 

or

DataFrameAnalysis outlierDetectionCustomized = OutlierDetection.builder() 
    .setMethod(OutlierDetection.Method.DISTANCE_KNN) 
    .setNNeighbors(5) 
    .build();

FetchContext object containing fields to be included in / excluded from the analysis

FetchSourceContext analyzedFields =
    new FetchSourceContext(
        true,
        new String[] { "included_field_1", "included_field_2" },
        new String[] { "excluded_field" });

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

PutDataFrameAnalyticsResponse response = client.machineLearning().putDataFrameAnalytics(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.

Executing a PutDataFrameAnalyticsRequest 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-data-frame-analytics method:

client.machineLearning().putDataFrameAnalyticsAsync(request, 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 put-data-frame-analytics looks like:

ActionListener<PutDataFrameAnalyticsResponse> listener = new ActionListener<PutDataFrameAnalyticsResponse>() {
    @Override
    public void onResponse(PutDataFrameAnalyticsResponse response) {
        
    }

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

The returned PutDataFrameAnalyticsResponse contains the newly created data frame analytics job.

DataFrameAnalyticsConfig createdConfig = response.getConfig();