This functionality is in beta and is subject to change. The design and code is less mature than official GA features and is being provided as-is with no warranties. Beta features are not subject to the support SLA of official GA features.
Explains the following about a data frame analytics config:
- field selection: which fields are included or not in the analysis
memory estimation: how much memory is estimated to be required. The estimate can be used when deciding the appropriate value for
model_memory_limitsetting later on.
The API accepts an
ExplainDataFrameAnalyticsRequest object and returns an
The request can be constructed with the id of an existing data frame analytics job.
It can also be constructed with a data frame analytics config to explain it before creating it.
DataFrameAnalyticsConfig config = DataFrameAnalyticsConfig.builder() .setSource(DataFrameAnalyticsSource.builder().setIndex("explain-df-test-source-index").build()) .setAnalysis(org.elasticsearch.client.ml.dataframe.OutlierDetection.createDefault()) .build(); request = new ExplainDataFrameAnalyticsRequest(config);
When executing a
ExplainDataFrameAnalyticsRequest in the following manner, the client waits
ExplainDataFrameAnalyticsResponse to be returned before continuing with code execution:
ExplainDataFrameAnalyticsResponse response = client.machineLearning().explainDataFrameAnalytics(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
5xx error code, the high-level
client tries to parse the response body error details instead and then throws
ElasticsearchException and adds the original
ResponseException as a
suppressed exception to it.
ExplainDataFrameAnalyticsRequest 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 explain-data-frame-analytics method:
The asynchronous method does not block and returns immediately. Once it is
ActionListener is called back using the
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
explain-data-frame-analytics looks like:
ExplainDataFrameAnalyticsResponse contains the field selection and the memory usage estimation.