stack es ml put-data-frame-analytics cli command

elastic stack es ml put-data-frame-analytics \
  --analysis <analysis> \
  --dest <dest> \
  --source <source> \
  --id <id> \
  [options]
		

Create a data frame analytics job.

Behaviour flags:

--dry-run — validate all inputs and exit without performing any action

--analysis string required

The analysis configuration, which contains the information necessary to perform one of the following types of analysis: classification, outlier detection, or regression.

--dest string required

The destination configuration.

--source string required

The configuration of how to source the analysis data.

--id string 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.

--allow-lazy-start

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.

--analyzed-fields string

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.

--description string

A description of the job.

--max-num-threads number

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.

--meta string

--model-memory-limit string

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.

--headers string

--version string

--error-trace

When set to true Elasticsearch will include the full stack trace of errors when they occur.

--filter-path string

Comma-separated list of filters in dot notation which reduce the response returned by Elasticsearch.

Repeatable: pass --filter-path multiple times to supply more than one value

--human

When set to true will return statistics in a format suitable for humans. For example "exists_time": "1h" for humans and "exists_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.

--pretty

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

--input-file string

path to a JSON file to use as command input

--dry-run

validate all inputs and exit without performing any action (preview changes without applying them)

--json

output as JSON