putdataframeanalytics

package
v8.17.0 Latest Latest
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 Go to latest Published: Dec 17, 2024 License: Apache-2.0 Imports: 12 Imported by: 4

Documentation

Overview

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.

Index

Constants

This section is empty.

Variables

View Source 
var ErrBuildPath = errors.New("cannot build path, check for missing path parameters")

ErrBuildPath is returned in case of missing parameters within the build of the request.

Functions

This section is empty.

Types

type NewPutDataFrameAnalytics 

type NewPutDataFrameAnalytics func(id string) *PutDataFrameAnalytics

NewPutDataFrameAnalytics type alias for index.

func NewPutDataFrameAnalyticsFunc 

func NewPutDataFrameAnalyticsFunc(tp elastictransport.Interface) NewPutDataFrameAnalytics

NewPutDataFrameAnalyticsFunc returns a new instance of PutDataFrameAnalytics with the provided transport. Used in the index of the library this allows to retrieve every apis in once place.

type PutDataFrameAnalytics 

type PutDataFrameAnalytics struct {
	// contains filtered or unexported fields
}

func New 

func New(tp elastictransport.Interface) *PutDataFrameAnalytics

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.

https://www.elastic.co/guide/en/elasticsearch/reference/current/put-dfanalytics.html

func (*PutDataFrameAnalytics) AllowLazyStart added in v8.9.0 

func (r *PutDataFrameAnalytics) AllowLazyStart(allowlazystart bool) *PutDataFrameAnalytics

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

func (*PutDataFrameAnalytics) Analysis added in v8.9.0 

func (r *PutDataFrameAnalytics) Analysis(analysis *types.DataframeAnalysisContainer) *PutDataFrameAnalytics

Analysis 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

func (*PutDataFrameAnalytics) AnalyzedFields added in v8.9.0 

func (r *PutDataFrameAnalytics) AnalyzedFields(analyzedfields *types.DataframeAnalysisAnalyzedFields) *PutDataFrameAnalytics

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

func (*PutDataFrameAnalytics) Description added in v8.9.0 

func (r *PutDataFrameAnalytics) Description(description string) *PutDataFrameAnalytics

Description A description of the job. API name: description

func (*PutDataFrameAnalytics) Dest added in v8.9.0 

func (r *PutDataFrameAnalytics) Dest(dest *types.DataframeAnalyticsDestination) *PutDataFrameAnalytics

Dest The destination configuration. API name: dest

func (PutDataFrameAnalytics) Do 

func (r PutDataFrameAnalytics) Do(providedCtx context.Context) (*Response, error)

Do runs the request through the transport, handle the response and returns a putdataframeanalytics.Response

func (*PutDataFrameAnalytics) ErrorTrace added in v8.14.0 

func (r *PutDataFrameAnalytics) ErrorTrace(errortrace bool) *PutDataFrameAnalytics

ErrorTrace When set to `true` Elasticsearch will include the full stack trace of errors when they occur. API name: error_trace

func (*PutDataFrameAnalytics) FilterPath added in v8.14.0 

func (r *PutDataFrameAnalytics) FilterPath(filterpaths ...string) *PutDataFrameAnalytics

FilterPath Comma-separated list of filters in dot notation which reduce the response returned by Elasticsearch. API name: filter_path

func (*PutDataFrameAnalytics) Header 

func (r *PutDataFrameAnalytics) Header(key, value string) *PutDataFrameAnalytics

Header set a key, value pair in the PutDataFrameAnalytics headers map.

func (*PutDataFrameAnalytics) Headers added in v8.9.0 

func (r *PutDataFrameAnalytics) Headers(httpheaders types.HttpHeaders) *PutDataFrameAnalytics

API name: headers

func (*PutDataFrameAnalytics) HttpRequest 

func (r *PutDataFrameAnalytics) HttpRequest(ctx context.Context) (*http.Request, error)

HttpRequest returns the http.Request object built from the given parameters.

func (*PutDataFrameAnalytics) Human added in v8.14.0 

func (r *PutDataFrameAnalytics) Human(human bool) *PutDataFrameAnalytics

Human When set to `true` will return statistics in a format suitable for humans. For example `"exists_time": "1h"` for humans and `"eixsts_time_in_millis": 3600000` for computers. When disabled the human readable values will be omitted. This makes sense for responses being consumed only by machines. API name: human

func (*PutDataFrameAnalytics) MaxNumThreads added in v8.9.0 

func (r *PutDataFrameAnalytics) MaxNumThreads(maxnumthreads int) *PutDataFrameAnalytics

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

func (*PutDataFrameAnalytics) Meta_ added in v8.17.0 

func (r *PutDataFrameAnalytics) Meta_(metadata types.Metadata) *PutDataFrameAnalytics

API name: _meta

func (*PutDataFrameAnalytics) ModelMemoryLimit added in v8.9.0 

func (r *PutDataFrameAnalytics) ModelMemoryLimit(modelmemorylimit string) *PutDataFrameAnalytics

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

func (PutDataFrameAnalytics) Perform added in v8.7.0 

func (r PutDataFrameAnalytics) Perform(providedCtx context.Context) (*http.Response, error)

Perform runs the http.Request through the provided transport and returns an http.Response.

func (*PutDataFrameAnalytics) Pretty added in v8.14.0 

func (r *PutDataFrameAnalytics) Pretty(pretty bool) *PutDataFrameAnalytics

Pretty If set to `true` the returned JSON will be "pretty-formatted". Only use this option for debugging only. API name: pretty

func (*PutDataFrameAnalytics) Raw 

func (r *PutDataFrameAnalytics) Raw(raw io.Reader) *PutDataFrameAnalytics

Raw takes a json payload as input which is then passed to the http.Request If specified Raw takes precedence on Request method.

func (*PutDataFrameAnalytics) Request 

func (r *PutDataFrameAnalytics) Request(req *Request) *PutDataFrameAnalytics

Request allows to set the request property with the appropriate payload.

func (*PutDataFrameAnalytics) Source added in v8.9.0 

func (r *PutDataFrameAnalytics) Source(source *types.DataframeAnalyticsSource) *PutDataFrameAnalytics

Source The configuration of how to source the analysis data. API name: source

func (*PutDataFrameAnalytics) Version added in v8.9.0 

func (r *PutDataFrameAnalytics) Version(versionstring string) *PutDataFrameAnalytics

API name: version

type Request 

type Request struct {

	// 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.
	AllowLazyStart *bool `json:"allow_lazy_start,omitempty"`
	// Analysis The analysis configuration, which contains the information necessary to
	// perform one of the following types of analysis: classification, outlier
	// detection, or regression.
	Analysis types.DataframeAnalysisContainer `json:"analysis"`
	// 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.
	AnalyzedFields *types.DataframeAnalysisAnalyzedFields `json:"analyzed_fields,omitempty"`
	// Description A description of the job.
	Description *string `json:"description,omitempty"`
	// Dest The destination configuration.
	Dest    types.DataframeAnalyticsDestination `json:"dest"`
	Headers types.HttpHeaders                   `json:"headers,omitempty"`
	// 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.
	MaxNumThreads *int           `json:"max_num_threads,omitempty"`
	Meta_         types.Metadata `json:"_meta,omitempty"`
	// 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.
	ModelMemoryLimit *string `json:"model_memory_limit,omitempty"`
	// Source The configuration of how to source the analysis data.
	Source  types.DataframeAnalyticsSource `json:"source"`
	Version *string                        `json:"version,omitempty"`
}

Request holds the request body struct for the package putdataframeanalytics

https://github.com/elastic/elasticsearch-specification/blob/2f823ff6fcaa7f3f0f9b990dc90512d8901e5d64/specification/ml/put_data_frame_analytics/MlPutDataFrameAnalyticsRequest.ts#L30-L144

func NewRequest added in v8.5.0 

func NewRequest() *Request

NewRequest returns a Request

func (*Request) FromJSON added in v8.5.0 

func (r *Request) FromJSON(data string) (*Request, error)

FromJSON allows to load an arbitrary json into the request structure

func (*Request) UnmarshalJSON added in v8.12.1 

func (s *Request) UnmarshalJSON(data []byte) error

type Response added in v8.7.0 

type Response struct {
	AllowLazyStart   bool                                   `json:"allow_lazy_start"`
	Analysis         types.DataframeAnalysisContainer       `json:"analysis"`
	AnalyzedFields   *types.DataframeAnalysisAnalyzedFields `json:"analyzed_fields,omitempty"`
	Authorization    *types.DataframeAnalyticsAuthorization `json:"authorization,omitempty"`
	CreateTime       int64                                  `json:"create_time"`
	Description      *string                                `json:"description,omitempty"`
	Dest             types.DataframeAnalyticsDestination    `json:"dest"`
	Id               string                                 `json:"id"`
	MaxNumThreads    int                                    `json:"max_num_threads"`
	Meta_            types.Metadata                         `json:"_meta,omitempty"`
	ModelMemoryLimit string                                 `json:"model_memory_limit"`
	Source           types.DataframeAnalyticsSource         `json:"source"`
	Version          string                                 `json:"version"`
}

Response holds the response body struct for the package putdataframeanalytics

https://github.com/elastic/elasticsearch-specification/blob/2f823ff6fcaa7f3f0f9b990dc90512d8901e5d64/specification/ml/put_data_frame_analytics/MlPutDataFrameAnalyticsResponse.ts#L31-L47

func NewResponse added in v8.7.0 

func NewResponse() *Response

NewResponse returns a Response

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