co.elastic.clients.elasticsearch.ml.PutDataFrameAnalyticsRequest Maven / Gradle / Ivy
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/*
* Licensed to Elasticsearch B.V. under one or more contributor
* license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright
* ownership. Elasticsearch B.V. licenses this file to you under
* the Apache License, Version 2.0 (the "License"); you may
* not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
package co.elastic.clients.elasticsearch.ml;
import co.elastic.clients.elasticsearch._types.ErrorResponse;
import co.elastic.clients.elasticsearch._types.RequestBase;
import co.elastic.clients.json.JsonpDeserializable;
import co.elastic.clients.json.JsonpDeserializer;
import co.elastic.clients.json.JsonpMapper;
import co.elastic.clients.json.JsonpSerializable;
import co.elastic.clients.json.ObjectBuilderDeserializer;
import co.elastic.clients.json.ObjectDeserializer;
import co.elastic.clients.transport.Endpoint;
import co.elastic.clients.transport.endpoints.SimpleEndpoint;
import co.elastic.clients.util.ApiTypeHelper;
import co.elastic.clients.util.ObjectBuilder;
import jakarta.json.stream.JsonGenerator;
import java.lang.Boolean;
import java.lang.Integer;
import java.lang.String;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Objects;
import java.util.function.Function;
import javax.annotation.Nullable;
//----------------------------------------------------------------
// THIS CODE IS GENERATED. MANUAL EDITS WILL BE LOST.
//----------------------------------------------------------------
//
// This code is generated from the Elasticsearch API specification
// at https://github.com/elastic/elasticsearch-specification
//
// Manual updates to this file will be lost when the code is
// re-generated.
//
// If you find a property that is missing or wrongly typed, please
// open an issue or a PR on the API specification repository.
//
//----------------------------------------------------------------
// typedef: ml.put_data_frame_analytics.Request
/**
* Create 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 API
* specification
*/
@JsonpDeserializable
public class PutDataFrameAnalyticsRequest extends RequestBase implements JsonpSerializable {
@Nullable
private final Boolean allowLazyStart;
private final DataframeAnalysis analysis;
@Nullable
private final DataframeAnalysisAnalyzedFields analyzedFields;
@Nullable
private final String description;
private final DataframeAnalyticsDestination dest;
private final Map> headers;
private final String id;
@Nullable
private final Integer maxNumThreads;
@Nullable
private final String modelMemoryLimit;
private final DataframeAnalyticsSource source;
@Nullable
private final String version;
// ---------------------------------------------------------------------------------------------
private PutDataFrameAnalyticsRequest(Builder builder) {
this.allowLazyStart = builder.allowLazyStart;
this.analysis = ApiTypeHelper.requireNonNull(builder.analysis, this, "analysis");
this.analyzedFields = builder.analyzedFields;
this.description = builder.description;
this.dest = ApiTypeHelper.requireNonNull(builder.dest, this, "dest");
this.headers = ApiTypeHelper.unmodifiable(builder.headers);
this.id = ApiTypeHelper.requireNonNull(builder.id, this, "id");
this.maxNumThreads = builder.maxNumThreads;
this.modelMemoryLimit = builder.modelMemoryLimit;
this.source = ApiTypeHelper.requireNonNull(builder.source, this, "source");
this.version = builder.version;
}
public static PutDataFrameAnalyticsRequest of(Function> fn) {
return fn.apply(new Builder()).build();
}
/**
* 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: {@code allow_lazy_start}
*/
@Nullable
public final Boolean allowLazyStart() {
return this.allowLazyStart;
}
/**
* 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: {@code analysis}
*/
public final DataframeAnalysis analysis() {
return this.analysis;
}
/**
* 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: {@code analyzed_fields}
*/
@Nullable
public final DataframeAnalysisAnalyzedFields analyzedFields() {
return this.analyzedFields;
}
/**
* A description of the job.
*
* API name: {@code description}
*/
@Nullable
public final String description() {
return this.description;
}
/**
* Required - The destination configuration.
*
* API name: {@code dest}
*/
public final DataframeAnalyticsDestination dest() {
return this.dest;
}
/**
* API name: {@code headers}
*/
public final Map> headers() {
return this.headers;
}
/**
* 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: {@code id}
*/
public final String id() {
return this.id;
}
/**
* 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: {@code max_num_threads}
*/
@Nullable
public final Integer maxNumThreads() {
return this.maxNumThreads;
}
/**
* 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: {@code model_memory_limit}
*/
@Nullable
public final String modelMemoryLimit() {
return this.modelMemoryLimit;
}
/**
* Required - The configuration of how to source the analysis data.
*
* API name: {@code source}
*/
public final DataframeAnalyticsSource source() {
return this.source;
}
/**
* API name: {@code version}
*/
@Nullable
public final String version() {
return this.version;
}
/**
* Serialize this object to JSON.
*/
public void serialize(JsonGenerator generator, JsonpMapper mapper) {
generator.writeStartObject();
serializeInternal(generator, mapper);
generator.writeEnd();
}
protected void serializeInternal(JsonGenerator generator, JsonpMapper mapper) {
if (this.allowLazyStart != null) {
generator.writeKey("allow_lazy_start");
generator.write(this.allowLazyStart);
}
generator.writeKey("analysis");
this.analysis.serialize(generator, mapper);
if (this.analyzedFields != null) {
generator.writeKey("analyzed_fields");
this.analyzedFields.serialize(generator, mapper);
}
if (this.description != null) {
generator.writeKey("description");
generator.write(this.description);
}
generator.writeKey("dest");
this.dest.serialize(generator, mapper);
if (ApiTypeHelper.isDefined(this.headers)) {
generator.writeKey("headers");
generator.writeStartObject();
for (Map.Entry> item0 : this.headers.entrySet()) {
generator.writeKey(item0.getKey());
generator.writeStartArray();
if (item0.getValue() != null) {
for (String item1 : item0.getValue()) {
generator.write(item1);
}
}
generator.writeEnd();
}
generator.writeEnd();
}
if (this.maxNumThreads != null) {
generator.writeKey("max_num_threads");
generator.write(this.maxNumThreads);
}
if (this.modelMemoryLimit != null) {
generator.writeKey("model_memory_limit");
generator.write(this.modelMemoryLimit);
}
generator.writeKey("source");
this.source.serialize(generator, mapper);
if (this.version != null) {
generator.writeKey("version");
generator.write(this.version);
}
}
// ---------------------------------------------------------------------------------------------
/**
* Builder for {@link PutDataFrameAnalyticsRequest}.
*/
public static class Builder extends RequestBase.AbstractBuilder
implements
ObjectBuilder {
@Nullable
private Boolean allowLazyStart;
private DataframeAnalysis analysis;
@Nullable
private DataframeAnalysisAnalyzedFields analyzedFields;
@Nullable
private String description;
private DataframeAnalyticsDestination dest;
@Nullable
private Map> headers;
private String id;
@Nullable
private Integer maxNumThreads;
@Nullable
private String modelMemoryLimit;
private DataframeAnalyticsSource source;
@Nullable
private String version;
/**
* 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: {@code allow_lazy_start}
*/
public final Builder allowLazyStart(@Nullable Boolean value) {
this.allowLazyStart = value;
return this;
}
/**
* 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: {@code analysis}
*/
public final Builder analysis(DataframeAnalysis value) {
this.analysis = value;
return this;
}
/**
* 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: {@code analysis}
*/
public final Builder analysis(Function> fn) {
return this.analysis(fn.apply(new DataframeAnalysis.Builder()).build());
}
/**
* 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: {@code analyzed_fields}
*/
public final Builder analyzedFields(@Nullable DataframeAnalysisAnalyzedFields value) {
this.analyzedFields = value;
return this;
}
/**
* 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: {@code analyzed_fields}
*/
public final Builder analyzedFields(
Function> fn) {
return this.analyzedFields(fn.apply(new DataframeAnalysisAnalyzedFields.Builder()).build());
}
/**
* A description of the job.
*
* API name: {@code description}
*/
public final Builder description(@Nullable String value) {
this.description = value;
return this;
}
/**
* Required - The destination configuration.
*
* API name: {@code dest}
*/
public final Builder dest(DataframeAnalyticsDestination value) {
this.dest = value;
return this;
}
/**
* Required - The destination configuration.
*
* API name: {@code dest}
*/
public final Builder dest(
Function> fn) {
return this.dest(fn.apply(new DataframeAnalyticsDestination.Builder()).build());
}
/**
* API name: {@code headers}
*
* Adds all entries of map to headers.
*/
public final Builder headers(Map> map) {
this.headers = _mapPutAll(this.headers, map);
return this;
}
/**
* API name: {@code headers}
*
* Adds an entry to headers.
*/
public final Builder headers(String key, List value) {
this.headers = _mapPut(this.headers, key, value);
return this;
}
/**
* 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: {@code id}
*/
public final Builder id(String value) {
this.id = value;
return this;
}
/**
* 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: {@code max_num_threads}
*/
public final Builder maxNumThreads(@Nullable Integer value) {
this.maxNumThreads = value;
return this;
}
/**
* 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: {@code model_memory_limit}
*/
public final Builder modelMemoryLimit(@Nullable String value) {
this.modelMemoryLimit = value;
return this;
}
/**
* Required - The configuration of how to source the analysis data.
*
* API name: {@code source}
*/
public final Builder source(DataframeAnalyticsSource value) {
this.source = value;
return this;
}
/**
* Required - The configuration of how to source the analysis data.
*
* API name: {@code source}
*/
public final Builder source(
Function> fn) {
return this.source(fn.apply(new DataframeAnalyticsSource.Builder()).build());
}
/**
* API name: {@code version}
*/
public final Builder version(@Nullable String value) {
this.version = value;
return this;
}
@Override
protected Builder self() {
return this;
}
/**
* Builds a {@link PutDataFrameAnalyticsRequest}.
*
* @throws NullPointerException
* if some of the required fields are null.
*/
public PutDataFrameAnalyticsRequest build() {
_checkSingleUse();
return new PutDataFrameAnalyticsRequest(this);
}
}
// ---------------------------------------------------------------------------------------------
/**
* Json deserializer for {@link PutDataFrameAnalyticsRequest}
*/
public static final JsonpDeserializer _DESERIALIZER = ObjectBuilderDeserializer
.lazy(Builder::new, PutDataFrameAnalyticsRequest::setupPutDataFrameAnalyticsRequestDeserializer);
protected static void setupPutDataFrameAnalyticsRequestDeserializer(
ObjectDeserializer op) {
op.add(Builder::allowLazyStart, JsonpDeserializer.booleanDeserializer(), "allow_lazy_start");
op.add(Builder::analysis, DataframeAnalysis._DESERIALIZER, "analysis");
op.add(Builder::analyzedFields, DataframeAnalysisAnalyzedFields._DESERIALIZER, "analyzed_fields");
op.add(Builder::description, JsonpDeserializer.stringDeserializer(), "description");
op.add(Builder::dest, DataframeAnalyticsDestination._DESERIALIZER, "dest");
op.add(Builder::headers, JsonpDeserializer.stringMapDeserializer(
JsonpDeserializer.arrayDeserializer(JsonpDeserializer.stringDeserializer())), "headers");
op.add(Builder::maxNumThreads, JsonpDeserializer.integerDeserializer(), "max_num_threads");
op.add(Builder::modelMemoryLimit, JsonpDeserializer.stringDeserializer(), "model_memory_limit");
op.add(Builder::source, DataframeAnalyticsSource._DESERIALIZER, "source");
op.add(Builder::version, JsonpDeserializer.stringDeserializer(), "version");
}
// ---------------------------------------------------------------------------------------------
/**
* Endpoint "{@code ml.put_data_frame_analytics}".
*/
public static final Endpoint _ENDPOINT = new SimpleEndpoint<>(
"es/ml.put_data_frame_analytics",
// Request method
request -> {
return "PUT";
},
// Request path
request -> {
final int _id = 1 << 0;
int propsSet = 0;
propsSet |= _id;
if (propsSet == (_id)) {
StringBuilder buf = new StringBuilder();
buf.append("/_ml");
buf.append("/data_frame");
buf.append("/analytics");
buf.append("/");
SimpleEndpoint.pathEncode(request.id, buf);
return buf.toString();
}
throw SimpleEndpoint.noPathTemplateFound("path");
},
// Path parameters
request -> {
Map params = new HashMap<>();
final int _id = 1 << 0;
int propsSet = 0;
propsSet |= _id;
if (propsSet == (_id)) {
params.put("id", request.id);
}
return params;
},
// Request parameters
request -> {
return Collections.emptyMap();
}, SimpleEndpoint.emptyMap(), true, PutDataFrameAnalyticsResponse._DESERIALIZER);
}