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
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Nested Class Summary
Nested Classes Modifier and Type Class Description static class
PutDataFrameAnalyticsRequest.Builder
Builder forPutDataFrameAnalyticsRequest
.Nested classes/interfaces inherited from class co.elastic.clients.elasticsearch._types.RequestBase
RequestBase.AbstractBuilder<BuilderT extends RequestBase.AbstractBuilder<BuilderT>>
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Field Summary
Fields Modifier and Type Field Description static JsonpDeserializer<PutDataFrameAnalyticsRequest>
_DESERIALIZER
Json deserializer forPutDataFrameAnalyticsRequest
static Endpoint<PutDataFrameAnalyticsRequest,PutDataFrameAnalyticsResponse,ErrorResponse>
_ENDPOINT
Endpoint "ml.put_data_frame_analytics
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Method Summary
Modifier and Type Method Description 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.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.DataframeAnalysisAnalyzedFields
analyzedFields()
Specifiesincludes
and/orexcludes
patterns to select which fields will be included in the analysis.java.lang.String
description()
A description of the job.DataframeAnalyticsDestination
dest()
Required - The destination configuration.java.util.Map<java.lang.String,java.util.List<java.lang.String>>
headers()
API name:headers
java.lang.String
id()
Required - Identifier for the data frame analytics job.java.lang.Integer
maxNumThreads()
The maximum number of threads to be used by the analysis.java.lang.String
modelMemoryLimit()
The approximate maximum amount of memory resources that are permitted for analytical processing.static PutDataFrameAnalyticsRequest
of(java.util.function.Function<PutDataFrameAnalyticsRequest.Builder,ObjectBuilder<PutDataFrameAnalyticsRequest>> fn)
void
serialize(jakarta.json.stream.JsonGenerator generator, JsonpMapper mapper)
Serialize this object to JSON.protected void
serializeInternal(jakarta.json.stream.JsonGenerator generator, JsonpMapper mapper)
protected static void
setupPutDataFrameAnalyticsRequestDeserializer(ObjectDeserializer<PutDataFrameAnalyticsRequest.Builder> op)
DataframeAnalyticsSource
source()
Required - The configuration of how to source the analysis data.java.lang.String
version()
API name:version
Methods inherited from class co.elastic.clients.elasticsearch._types.RequestBase
toString
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
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Field Details
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_DESERIALIZER
Json deserializer forPutDataFrameAnalyticsRequest
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_ENDPOINT
public static final Endpoint<PutDataFrameAnalyticsRequest,PutDataFrameAnalyticsResponse,ErrorResponse> _ENDPOINTEndpoint "ml.put_data_frame_analytics
".
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Method Details
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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 tofalse
and a machine learning node with capacity to run the job cannot be immediately found, the API returns an error. If set totrue
, 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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analyzedFields
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
@Nullable public final java.lang.String 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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headers
public final java.util.Map<java.lang.String,java.util.List<java.lang.String>> headers()API name:headers
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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
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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
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modelMemoryLimit
@Nullable public final java.lang.String 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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version
@Nullable public final java.lang.String version()API name:version
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serialize
Serialize this object to JSON.- Specified by:
serialize
in interfaceJsonpSerializable
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serializeInternal
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setupPutDataFrameAnalyticsRequestDeserializer
protected static void setupPutDataFrameAnalyticsRequestDeserializer(ObjectDeserializer<PutDataFrameAnalyticsRequest.Builder> op)
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