import { Datafeed , Job } from '../../../../../plugins/ml/common/types/anomaly_detection_jobs' ; export const JOB_CONFIG = { job_id : `fq_multi_1_ae` , description : 'mean/min/max(responsetime) partition=airline on farequote dataset with 1h bucket span' , groups : [ 'farequote' , 'automated' , 'multi-metric' ] , analysis_config : { bucket_span : '1h' , influencers : [ 'airline' ] , detectors : [ { function : 'mean' , field_name : 'responsetime' , partition_field_name : 'airline' } , { function : 'min' , field_name : 'responsetime' , partition_field_name : 'airline' } , { function : 'max' , field_name : 'responsetime' , partition_field_name : 'airline' } , ] , } , data_description : { time_field : '@timestamp' } , analysis_limits : { model_memory_limit : '20mb' } , model_plot_config : { enabled : true } , } ; export const DATAFEED_CONFIG = { datafeed_id : 'datafeed-fq_multi_1_ae' , indices : [ 'ft_farequote' ] , job_id : 'fq_multi_1_ae' , query : { bool : { must : [ { match_all : { } } ] } } , } ; export const ML_EMBEDDABLE_TYPES = { ANOMALY_SWIMLANE : 'ml_anomaly_swimlane' , ANOMALY_CHARTS : 'ml_anomaly_charts' , } as const ;