Class PutDataFrameAnalyticsRequest.Builder
java.lang.Object
co.elastic.clients.util.ObjectBuilderBase
co.elastic.clients.util.WithJsonObjectBuilderBase<BuilderT>
co.elastic.clients.elasticsearch._types.RequestBase.AbstractBuilder<PutDataFrameAnalyticsRequest.Builder>
co.elastic.clients.elasticsearch.ml.PutDataFrameAnalyticsRequest.Builder
- All Implemented Interfaces:
WithJson<PutDataFrameAnalyticsRequest.Builder>
,ObjectBuilder<PutDataFrameAnalyticsRequest>
- Enclosing class:
- PutDataFrameAnalyticsRequest
public static class PutDataFrameAnalyticsRequest.Builder
extends RequestBase.AbstractBuilder<PutDataFrameAnalyticsRequest.Builder>
implements ObjectBuilder<PutDataFrameAnalyticsRequest>
Builder for
PutDataFrameAnalyticsRequest
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Constructor Summary
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Method Summary
Modifier and TypeMethodDescriptionallowLazyStart
(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.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.Required - The analysis configuration, which contains the information necessary to perform one of the following types of analysis: classification, outlier detection, or regression.Specifiesincludes
and/orexcludes
patterns to select which fields will be included in the analysis.analyzedFields
(Function<DataframeAnalysisAnalyzedFields.Builder, ObjectBuilder<DataframeAnalysisAnalyzedFields>> fn) Specifiesincludes
and/orexcludes
patterns to select which fields will be included in the analysis.build()
Builds aPutDataFrameAnalyticsRequest
.description
(String value) A description of the job.Required - The destination configuration.dest
(Function<DataframeAnalyticsDestination.Builder, ObjectBuilder<DataframeAnalyticsDestination>> fn) Required - The destination configuration.Required - Identifier for the data frame analytics job.maxNumThreads
(Integer value) The maximum number of threads to be used by the analysis.modelMemoryLimit
(String value) The approximate maximum amount of memory resources that are permitted for analytical processing.protected PutDataFrameAnalyticsRequest.Builder
self()
source
(DataframeAnalyticsSource value) Required - The configuration of how to source the analysis data.Required - The configuration of how to source the analysis data.Methods inherited from class co.elastic.clients.util.WithJsonObjectBuilderBase
withJson
Methods inherited from class co.elastic.clients.util.ObjectBuilderBase
_checkSingleUse, _listAdd, _listAddAll, _mapPut, _mapPutAll
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Constructor Details
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Builder
public Builder()
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Method Details
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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 thestarting
state until sufficient machine learning node capacity is available. This behavior is also affected by the cluster-widexpack.ml.max_lazy_ml_nodes
setting.API name:
allow_lazy_start
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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
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analysis
public final PutDataFrameAnalyticsRequest.Builder analysis(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
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analyzedFields
public final PutDataFrameAnalyticsRequest.Builder analyzedFields(@Nullable DataframeAnalysisAnalyzedFields value) Specifiesincludes
and/orexcludes
patterns to select which fields will be included in the analysis. The patterns specified inexcludes
are applied last, thereforeexcludes
takes precedence. In other words, if the same field is specified in bothincludes
andexcludes
, then the field will not be included in the analysis. Ifanalyzed_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 orboolean
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 thedest
index may contain documents that don’t have an outlier score. Regression supports fields that are numeric,boolean
,text
,keyword
, andip
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 thedest
index that don’t contain a results field are not included in the regression analysis. Classification supports fields that are numeric,boolean
,text
,keyword
, andip
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 thedest
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 as0-14 = 0
,15-24 = 1
,25-34 = 2
, and so on.API name:
analyzed_fields
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analyzedFields
public final PutDataFrameAnalyticsRequest.Builder analyzedFields(Function<DataframeAnalysisAnalyzedFields.Builder, ObjectBuilder<DataframeAnalysisAnalyzedFields>> fn) Specifiesincludes
and/orexcludes
patterns to select which fields will be included in the analysis. The patterns specified inexcludes
are applied last, thereforeexcludes
takes precedence. In other words, if the same field is specified in bothincludes
andexcludes
, then the field will not be included in the analysis. Ifanalyzed_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 orboolean
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 thedest
index may contain documents that don’t have an outlier score. Regression supports fields that are numeric,boolean
,text
,keyword
, andip
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 thedest
index that don’t contain a results field are not included in the regression analysis. Classification supports fields that are numeric,boolean
,text
,keyword
, andip
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 thedest
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 as0-14 = 0
,15-24 = 1
,25-34 = 2
, and so on.API name:
analyzed_fields
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description
A description of the job.API name:
description
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dest
Required - The destination configuration.API name:
dest
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dest
public final PutDataFrameAnalyticsRequest.Builder dest(Function<DataframeAnalyticsDestination.Builder, ObjectBuilder<DataframeAnalyticsDestination>> fn) Required - The destination configuration.API name:
dest
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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
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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
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modelMemoryLimit
The approximate maximum amount of memory resources that are permitted for analytical processing. If yourelasticsearch.yml
file contains anxpack.ml.max_model_memory_limit
setting, an error occurs when you try to create data frame analytics jobs that havemodel_memory_limit
values greater than that setting.API name:
model_memory_limit
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source
Required - The configuration of how to source the analysis data.API name:
source
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source
public final PutDataFrameAnalyticsRequest.Builder source(Function<DataframeAnalyticsSource.Builder, ObjectBuilder<DataframeAnalyticsSource>> fn) Required - The configuration of how to source the analysis data.API name:
source
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self
- Specified by:
self
in classRequestBase.AbstractBuilder<PutDataFrameAnalyticsRequest.Builder>
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build
Builds aPutDataFrameAnalyticsRequest
.- Specified by:
build
in interfaceObjectBuilder<PutDataFrameAnalyticsRequest>
- Throws:
NullPointerException
- if some of the required fields are null.
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