[role="xpack"] [testenv="platinum"] [[put-dfanalytics]] === Create {dfanalytics-jobs} API [subs="attributes"] ++++ Create {dfanalytics-jobs} ++++ Instantiates a {dfanalytics-job}. experimental[] [[ml-put-dfanalytics-request]] ==== {api-request-title} `PUT _ml/data_frame/analytics/` [[ml-put-dfanalytics-prereq]] ==== {api-prereq-title} * You must have `machine_learning_admin` built-in role to use this API. You must also have `read` and `view_index_metadata` privileges on the source index and `read`, `create_index`, and `index` privileges on the destination index. For more information, see {stack-ov}/security-privileges.html[Security privileges] and {stack-ov}/built-in-roles.html[Built-in roles]. [[ml-put-dfanalytics-desc]] ==== {api-description-title} This API creates a {dfanalytics-job} that performs an analysis on the source index and stores the outcome in a destination index. The destination index will be automatically created if it does not exist. The `index.number_of_shards` and `index.number_of_replicas` settings of the source index will be copied over the destination index. When the source index matches multiple indices, these settings will be set to the maximum values found in the source indices. The mappings of the source indices are also attempted to be copied over to the destination index, however, if the mappings of any of the fields don't match among the source indices, the attempt will fail with an error message. If the destination index already exists, then it will be use as is. This makes it possible to set up the destination index in advance with custom settings and mappings. [[ml-put-dfanalytics-path-params]] ==== {api-path-parms-title} ``:: (Required, string) A numerical character string that uniquely identifies the {dfanalytics-job}. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters. [[ml-put-dfanalytics-request-body]] ==== {api-request-body-title} `analysis`:: (Required, object) Defines the type of {dfanalytics} you want to perform on your source index. For example: `outlier_detection`. See <>. `analyzed_fields`:: (Optional, object) You can specify both `includes` and/or `excludes` patterns. If `analyzed_fields` is not set, only the relevant fields will be included. For example, all the numeric fields for {oldetection}. `analyzed_fields.includes`::: (Optional, array) An array of strings that defines the fields that will be included in the analysis. `analyzed_fields.excludes`::: (Optional, array) An array of strings that defines the fields that will be excluded from the analysis. `description`:: (Optional, string) A description of the job. `dest`:: (Required, object) The destination configuration, consisting of `index` and optionally `results_field` (`ml` by default). `index`::: (Required, string) Defines the _destination index_ to store the results of the {dfanalytics-job}. `results_field`::: (Optional, string) Defines the name of the field in which to store the results of the analysis. Default to `ml`. `model_memory_limit`:: (Optional, string) The approximate maximum amount of memory resources that are permitted for analytical processing. The default value for {dfanalytics-jobs} is `1gb`. If your `elasticsearch.yml` file contains an `xpack.ml.max_model_memory_limit` setting, an error occurs when you try to create {dfanalytics-jobs} that have `model_memory_limit` values greater than that setting. For more information, see <>. `source`:: (Required, object) The source configuration, consisting of `index` and optionally a `query`. `index`::: (Required, string or array) Index or indices on which to perform the analysis. It can be a single index or index pattern as well as an array of indices or patterns. `query`::: (Optional, object) The {es} query domain-specific language (<>). This value corresponds to the query object in an {es} search POST body. All the options that are supported by {es} can be used, as this object is passed verbatim to {es}. By default, this property has the following value: `{"match_all": {}}`. [[ml-put-dfanalytics-example]] ==== {api-examples-title} The following example creates the `loganalytics` {dfanalytics-job}, the analysis type is `outlier_detection`: [source,console] -------------------------------------------------- PUT _ml/data_frame/analytics/loganalytics { "description": "Outlier detection on log data", "source": { "index": "logdata" }, "dest": { "index": "logdata_out" }, "analysis": { "outlier_detection": { } } } -------------------------------------------------- // TEST[setup:setup_logdata] The API returns the following result: [source,console-result] ---- { "id" : "loganalytics", "description": "Outlier detection on log data", "source" : { "index" : [ "logdata" ], "query" : { "match_all" : { } } }, "dest" : { "index" : "logdata_out", "results_field" : "ml" }, "analysis" : { "outlier_detection" : { } }, "model_memory_limit" : "1gb", "create_time" : 1562351429434, "version" : "7.3.0" } ---- // TESTRESPONSE[s/1562351429434/$body.$_path/] // TESTRESPONSE[s/"version" : "7.3.0"/"version" : $body.version/]