MlEstimateModelMemoryRequest.ts

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import { AnalysisConfig } from '@ml/_types/Analysis'
import { Dictionary } from '@spec_utils/Dictionary'
import { RequestBase } from '@_types/Base'
import { Field } from '@_types/common'
import { long } from '@_types/Numeric'

/**
 * Makes an estimation of the memory usage for an anomaly detection job model.
 * It is based on analysis configuration details for the job and cardinality
 * estimates for the fields it references.
 * @rest_spec_name ml.estimate_model_memory
 * @since 7.7.0
 * @stability stable
 * @cluster_privileges manage_ml
 */
export interface Request extends RequestBase {
  body: {
    /**
     * For a list of the properties that you can specify in the
     * `analysis_config` component of the body of this API.
     */
    analysis_config?: AnalysisConfig
    /**
     * Estimates of the highest cardinality in a single bucket that is observed
     * for influencer fields over the time period that the job analyzes data.
     * To produce a good answer, values must be provided for all influencer
     * fields. Providing values for fields that are not listed as `influencers`
     * has no effect on the estimation.
     */
    max_bucket_cardinality?: Dictionary<Field, long>
    /**
     *  Estimates of the cardinality that is observed for fields over the whole
     * time period that the job analyzes data. To produce a good answer, values
     * must be provided for fields referenced in the `by_field_name`,
     * `over_field_name` and `partition_field_name` of any detectors. Providing
     * values for other fields has no effect on the estimation. It can be
     * omitted from the request if no detectors have a `by_field_name`,
     * `over_field_name` or `partition_field_name`.
     */
    overall_cardinality?: Dictionary<Field, long>
  }
}