Package co.elastic.clients.elasticsearch.ml

Class PutDataFrameAnalyticsRequest

java.lang.Object
co.elastic.clients.elasticsearch._types.RequestBase
co.elastic.clients.elasticsearch.ml.PutDataFrameAnalyticsRequest
All Implemented Interfaces:
JsonpSerializable
@JsonpDeserializable
public class PutDataFrameAnalyticsRequest
extends RequestBase
implements JsonpSerializable
Instantiates a data frame analytics job. This API creates a data frame analytics job that performs an analysis on the source indices and stores the outcome in a destination index.
See Also:
API specification

  • Field Details

  • Method Details

    • of

      public static PutDataFrameAnalyticsRequest of​(java.util.function.Function<PutDataFrameAnalyticsRequest.Builder,​ObjectBuilder<PutDataFrameAnalyticsRequest>> fn)

    • allowLazyStart

      @Nullable public final java.lang.Boolean allowLazyStart()
      Specifies whether this job can start when there is insufficient machine learning node capacity for it to be immediately assigned to a node. If set to false and a machine learning node with capacity to run the job cannot be immediately found, the API returns an error. If set to true, the API does not return an error; the job waits in the starting state until sufficient machine learning node capacity is available. This behavior is also affected by the cluster-wide xpack.ml.max_lazy_ml_nodes setting.

      API name: allow_lazy_start

    • analysis

      public final DataframeAnalysis analysis()
      Required - The analysis configuration, which contains the information necessary to perform one of the following types of analysis: classification, outlier detection, or regression.

      API name: analysis

    • analyzedFields

      @Nullable public final DataframeAnalysisAnalyzedFields analyzedFields()
      Specifies includes and/or excludes patterns to select which fields will be included in the analysis. The patterns specified in excludes are applied last, therefore excludes takes precedence. In other words, if the same field is specified in both includes and excludes, then the field will not be included in the analysis. If analyzed_fields is not set, only the relevant fields will be included. For example, all the numeric fields for outlier detection. The supported fields vary for each type of analysis. Outlier detection requires numeric or boolean data to analyze. The algorithms don’t support missing values therefore fields that have data types other than numeric or boolean are ignored. Documents where included fields contain missing values, null values, or an array are also ignored. Therefore the dest index may contain documents that don’t have an outlier score. Regression supports fields that are numeric, boolean, text, keyword, and ip data types. It is also tolerant of missing values. Fields that are supported are included in the analysis, other fields are ignored. Documents where included fields contain an array with two or more values are also ignored. Documents in the dest index that don’t contain a results field are not included in the regression analysis. Classification supports fields that are numeric, boolean, text, keyword, and ip data types. It is also tolerant of missing values. Fields that are supported are included in the analysis, other fields are ignored. Documents where included fields contain an array with two or more values are also ignored. Documents in the dest index that don’t contain a results field are not included in the classification analysis. Classification analysis can be improved by mapping ordinal variable values to a single number. For example, in case of age ranges, you can model the values as 0-14 = 0, 15-24 = 1, 25-34 = 2, and so on.

      API name: analyzed_fields

    • description

      @Nullable public final java.lang.String description()
      A description of the job.

      API name: description

    • dest

      public final DataframeAnalyticsDestination dest()
      Required - The destination configuration.

      API name: dest

    • headers

      public final java.util.Map<java.lang.String,​java.util.List<java.lang.String>> headers()
      API name: headers

    • id

      public final java.lang.String id()
      Required - Identifier for the data frame analytics job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.

      API name: id

    • maxNumThreads

      @Nullable public final java.lang.Integer maxNumThreads()
      The maximum number of threads to be used by the analysis. Using more threads may decrease the time necessary to complete the analysis at the cost of using more CPU. Note that the process may use additional threads for operational functionality other than the analysis itself.

      API name: max_num_threads

    • modelMemoryLimit

      @Nullable public final java.lang.String modelMemoryLimit()
      The approximate maximum amount of memory resources that are permitted for analytical processing. If your elasticsearch.yml file contains an xpack.ml.max_model_memory_limit setting, an error occurs when you try to create data frame analytics jobs that have model_memory_limit values greater than that setting.

      API name: model_memory_limit

    • source

      public final DataframeAnalyticsSource source()
      Required - The configuration of how to source the analysis data.

      API name: source

    • version

      @Nullable public final java.lang.String version()
      API name: version

    • serialize

      public void serialize​(jakarta.json.stream.JsonGenerator generator, JsonpMapper mapper)
      Serialize this object to JSON.
      Specified by:
      serialize in interface JsonpSerializable

    • serializeInternal

      protected void serializeInternal​(jakarta.json.stream.JsonGenerator generator, JsonpMapper mapper)

    • setupPutDataFrameAnalyticsRequestDeserializer

      protected static void setupPutDataFrameAnalyticsRequestDeserializer​(ObjectDeserializer<PutDataFrameAnalyticsRequest.Builder> op)