Package co.elastic.clients.elasticsearch.ml

Class PutDataFrameAnalyticsRequest.Builder

java.lang.Object
co.elastic.clients.util.ObjectBuilderBase
co.elastic.clients.elasticsearch.ml.PutDataFrameAnalyticsRequest.Builder
All Implemented Interfaces:
ObjectBuilder<PutDataFrameAnalyticsRequest>
Enclosing class:
PutDataFrameAnalyticsRequest
public static class PutDataFrameAnalyticsRequest.Builder
extends ObjectBuilderBase
implements ObjectBuilder<PutDataFrameAnalyticsRequest>
Builder for PutDataFrameAnalyticsRequest.

  • Constructor Details

    • Builder

      public Builder()

  • Method Details

    • allowLazyStart

      public final PutDataFrameAnalyticsRequest.Builder allowLazyStart​(@Nullable java.lang.Boolean value)
      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 PutDataFrameAnalyticsRequest.Builder analysis​(DataframeAnalysis value)
      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

    • analysis

      public final PutDataFrameAnalyticsRequest.Builder analysis​(java.util.function.Function<DataframeAnalysis.Builder,​ObjectBuilder<DataframeAnalysis>> fn)
      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

      public final PutDataFrameAnalyticsRequest.Builder analyzedFields​(@Nullable DataframeAnalysisAnalyzedFields value)
      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

    • analyzedFields

      public final PutDataFrameAnalyticsRequest.Builder analyzedFields​(java.util.function.Function<DataframeAnalysisAnalyzedFields.Builder,​ObjectBuilder<DataframeAnalysisAnalyzedFields>> fn)
      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

      public final PutDataFrameAnalyticsRequest.Builder description​(@Nullable java.lang.String value)
      A description of the job.

      API name: description

    • dest

      public final PutDataFrameAnalyticsRequest.Builder dest​(DataframeAnalyticsDestination value)
      Required - The destination configuration.

      API name: dest

    • dest

      public final PutDataFrameAnalyticsRequest.Builder dest​(java.util.function.Function<DataframeAnalyticsDestination.Builder,​ObjectBuilder<DataframeAnalyticsDestination>> fn)
      Required - The destination configuration.

      API name: dest

    • id

      public final PutDataFrameAnalyticsRequest.Builder id​(java.lang.String value)
      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

      public final PutDataFrameAnalyticsRequest.Builder maxNumThreads​(@Nullable java.lang.Integer value)
      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

      public final PutDataFrameAnalyticsRequest.Builder modelMemoryLimit​(@Nullable java.lang.String value)
      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 PutDataFrameAnalyticsRequest.Builder source​(DataframeAnalyticsSource value)
      Required - The configuration of how to source the analysis data.

      API name: source

    • source

      public final PutDataFrameAnalyticsRequest.Builder source​(java.util.function.Function<DataframeAnalyticsSource.Builder,​ObjectBuilder<DataframeAnalyticsSource>> fn)
      Required - The configuration of how to source the analysis data.

      API name: source

    • build

      public PutDataFrameAnalyticsRequest build()
      Builds a PutDataFrameAnalyticsRequest.
      Specified by:
      build in interface ObjectBuilder<PutDataFrameAnalyticsRequest>
      Throws:
      java.lang.NullPointerException - if some of the required fields are null.