// SPDX-License-Identifier: GPL-3.0-or-later #include "ad_charts.h" void ml_update_dimensions_chart(ml_host_t *host, const ml_machine_learning_stats_t &mls) { /* * Machine learning status */ if (Cfg.enable_statistics_charts) { if (!host->machine_learning_status_rs) { char id_buf[1024]; char name_buf[1024]; snprintfz(id_buf, 1024, "machine_learning_status_on_%s", localhost->machine_guid); snprintfz(name_buf, 1024, "machine_learning_status_on_%s", rrdhost_hostname(localhost)); host->machine_learning_status_rs = rrdset_create( host->rh, "netdata", // type id_buf, name_buf, // name NETDATA_ML_CHART_FAMILY, // family "netdata.machine_learning_status", // ctx "Machine learning status", // title "dimensions", // units NETDATA_ML_PLUGIN, // plugin NETDATA_ML_MODULE_TRAINING, // module NETDATA_ML_CHART_PRIO_MACHINE_LEARNING_STATUS, // priority localhost->rrd_update_every, // update_every RRDSET_TYPE_LINE // chart_type ); rrdset_flag_set(host->machine_learning_status_rs , RRDSET_FLAG_ANOMALY_DETECTION); host->machine_learning_status_enabled_rd = rrddim_add(host->machine_learning_status_rs, "enabled", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); host->machine_learning_status_disabled_sp_rd = rrddim_add(host->machine_learning_status_rs, "disabled-sp", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); } rrddim_set_by_pointer(host->machine_learning_status_rs, host->machine_learning_status_enabled_rd, mls.num_machine_learning_status_enabled); rrddim_set_by_pointer(host->machine_learning_status_rs, host->machine_learning_status_disabled_sp_rd, mls.num_machine_learning_status_disabled_sp); rrdset_done(host->machine_learning_status_rs); } /* * Metric type */ if (Cfg.enable_statistics_charts) { if (!host->metric_type_rs) { char id_buf[1024]; char name_buf[1024]; snprintfz(id_buf, 1024, "metric_types_on_%s", localhost->machine_guid); snprintfz(name_buf, 1024, "metric_types_on_%s", rrdhost_hostname(localhost)); host->metric_type_rs = rrdset_create( host->rh, "netdata", // type id_buf, // id name_buf, // name NETDATA_ML_CHART_FAMILY, // family "netdata.metric_types", // ctx "Dimensions by metric type", // title "dimensions", // units NETDATA_ML_PLUGIN, // plugin NETDATA_ML_MODULE_TRAINING, // module NETDATA_ML_CHART_PRIO_METRIC_TYPES, // priority localhost->rrd_update_every, // update_every RRDSET_TYPE_LINE // chart_type ); rrdset_flag_set(host->metric_type_rs, RRDSET_FLAG_ANOMALY_DETECTION); host->metric_type_constant_rd = rrddim_add(host->metric_type_rs, "constant", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); host->metric_type_variable_rd = rrddim_add(host->metric_type_rs, "variable", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); } rrddim_set_by_pointer(host->metric_type_rs, host->metric_type_constant_rd, mls.num_metric_type_constant); rrddim_set_by_pointer(host->metric_type_rs, host->metric_type_variable_rd, mls.num_metric_type_variable); rrdset_done(host->metric_type_rs); } /* * Training status */ if (Cfg.enable_statistics_charts) { if (!host->training_status_rs) { char id_buf[1024]; char name_buf[1024]; snprintfz(id_buf, 1024, "training_status_on_%s", localhost->machine_guid); snprintfz(name_buf, 1024, "training_status_on_%s", rrdhost_hostname(localhost)); host->training_status_rs = rrdset_create( host->rh, "netdata", // type id_buf, // id name_buf, // name NETDATA_ML_CHART_FAMILY, // family "netdata.training_status", // ctx "Training status of dimensions", // title "dimensions", // units NETDATA_ML_PLUGIN, // plugin NETDATA_ML_MODULE_TRAINING, // module NETDATA_ML_CHART_PRIO_TRAINING_STATUS, // priority localhost->rrd_update_every, // update_every RRDSET_TYPE_LINE // chart_type ); rrdset_flag_set(host->training_status_rs, RRDSET_FLAG_ANOMALY_DETECTION); host->training_status_untrained_rd = rrddim_add(host->training_status_rs, "untrained", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); host->training_status_pending_without_model_rd = rrddim_add(host->training_status_rs, "pending-without-model", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); host->training_status_trained_rd = rrddim_add(host->training_status_rs, "trained", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); host->training_status_pending_with_model_rd = rrddim_add(host->training_status_rs, "pending-with-model", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); host->training_status_silenced_rd = rrddim_add(host->training_status_rs, "silenced", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); } rrddim_set_by_pointer(host->training_status_rs, host->training_status_untrained_rd, mls.num_training_status_untrained); rrddim_set_by_pointer(host->training_status_rs, host->training_status_pending_without_model_rd, mls.num_training_status_pending_without_model); rrddim_set_by_pointer(host->training_status_rs, host->training_status_trained_rd, mls.num_training_status_trained); rrddim_set_by_pointer(host->training_status_rs, host->training_status_pending_with_model_rd, mls.num_training_status_pending_with_model); rrddim_set_by_pointer(host->training_status_rs, host->training_status_silenced_rd, mls.num_training_status_silenced); rrdset_done(host->training_status_rs); } /* * Prediction status */ { if (!host->dimensions_rs) { char id_buf[1024]; char name_buf[1024]; snprintfz(id_buf, 1024, "dimensions_on_%s", localhost->machine_guid); snprintfz(name_buf, 1024, "dimensions_on_%s", rrdhost_hostname(localhost)); host->dimensions_rs = rrdset_create( host->rh, "anomaly_detection", // type id_buf, // id name_buf, // name "dimensions", // family "anomaly_detection.dimensions", // ctx "Anomaly detection dimensions", // title "dimensions", // units NETDATA_ML_PLUGIN, // plugin NETDATA_ML_MODULE_TRAINING, // module ML_CHART_PRIO_DIMENSIONS, // priority localhost->rrd_update_every, // update_every RRDSET_TYPE_LINE // chart_type ); rrdset_flag_set(host->dimensions_rs, RRDSET_FLAG_ANOMALY_DETECTION); host->dimensions_anomalous_rd = rrddim_add(host->dimensions_rs, "anomalous", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); host->dimensions_normal_rd = rrddim_add(host->dimensions_rs, "normal", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); } rrddim_set_by_pointer(host->dimensions_rs, host->dimensions_anomalous_rd, mls.num_anomalous_dimensions); rrddim_set_by_pointer(host->dimensions_rs, host->dimensions_normal_rd, mls.num_normal_dimensions); rrdset_done(host->dimensions_rs); } // ML running { if (!host->ml_running_rs) { char id_buf[1024]; char name_buf[1024]; snprintfz(id_buf, 1024, "ml_running_on_%s", localhost->machine_guid); snprintfz(name_buf, 1024, "ml_running_on_%s", rrdhost_hostname(localhost)); host->ml_running_rs = rrdset_create( host->rh, "anomaly_detection", // type id_buf, // id name_buf, // name "anomaly_detection", // family "anomaly_detection.ml_running", // ctx "ML running", // title "boolean", // units NETDATA_ML_PLUGIN, // plugin NETDATA_ML_MODULE_DETECTION, // module NETDATA_ML_CHART_RUNNING, // priority localhost->rrd_update_every, // update_every RRDSET_TYPE_LINE // chart_type ); rrdset_flag_set(host->ml_running_rs, RRDSET_FLAG_ANOMALY_DETECTION); host->ml_running_rd = rrddim_add(host->ml_running_rs, "ml_running", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); } rrddim_set_by_pointer(host->ml_running_rs, host->ml_running_rd, host->ml_running); rrdset_done(host->ml_running_rs); } } void ml_update_host_and_detection_rate_charts(ml_host_t *host, collected_number AnomalyRate) { /* * Host anomaly rate */ { if (!host->anomaly_rate_rs) { char id_buf[1024]; char name_buf[1024]; snprintfz(id_buf, 1024, "anomaly_rate_on_%s", localhost->machine_guid); snprintfz(name_buf, 1024, "anomaly_rate_on_%s", rrdhost_hostname(localhost)); host->anomaly_rate_rs = rrdset_create( host->rh, "anomaly_detection", // type id_buf, // id name_buf, // name "anomaly_rate", // family "anomaly_detection.anomaly_rate", // ctx "Percentage of anomalous dimensions", // title "percentage", // units NETDATA_ML_PLUGIN, // plugin NETDATA_ML_MODULE_DETECTION, // module ML_CHART_PRIO_ANOMALY_RATE, // priority localhost->rrd_update_every, // update_every RRDSET_TYPE_LINE // chart_type ); rrdset_flag_set(host->anomaly_rate_rs, RRDSET_FLAG_ANOMALY_DETECTION); host->anomaly_rate_rd = rrddim_add(host->anomaly_rate_rs, "anomaly_rate", NULL, 1, 100, RRD_ALGORITHM_ABSOLUTE); } rrddim_set_by_pointer(host->anomaly_rate_rs, host->anomaly_rate_rd, AnomalyRate); rrdset_done(host->anomaly_rate_rs); } /* * Type anomaly rate */ { if (!host->type_anomaly_rate_rs) { char id_buf[1024]; char name_buf[1024]; snprintfz(id_buf, 1024, "type_anomaly_rate_on_%s", localhost->machine_guid); snprintfz(name_buf, 1024, "type_anomaly_rate_on_%s", rrdhost_hostname(localhost)); host->type_anomaly_rate_rs = rrdset_create( host->rh, "anomaly_detection", // type id_buf, // id name_buf, // name "anomaly_rate", // family "anomaly_detection.type_anomaly_rate", // ctx "Percentage of anomalous dimensions by type", // title "percentage", // units NETDATA_ML_PLUGIN, // plugin NETDATA_ML_MODULE_DETECTION, // module ML_CHART_PRIO_TYPE_ANOMALY_RATE, // priority localhost->rrd_update_every, // update_every RRDSET_TYPE_STACKED // chart_type ); rrdset_flag_set(host->type_anomaly_rate_rs, RRDSET_FLAG_ANOMALY_DETECTION); } for (auto &entry : host->type_anomaly_rate) { ml_type_anomaly_rate_t &type_anomaly_rate = entry.second; if (!type_anomaly_rate.rd) type_anomaly_rate.rd = rrddim_add(host->type_anomaly_rate_rs, string2str(entry.first), NULL, 1, 100, RRD_ALGORITHM_ABSOLUTE); double ar = 0.0; size_t n = type_anomaly_rate.anomalous_dimensions + type_anomaly_rate.normal_dimensions; if (n) ar = static_cast(type_anomaly_rate.anomalous_dimensions) / n; rrddim_set_by_pointer(host->type_anomaly_rate_rs, type_anomaly_rate.rd, ar * 10000.0); type_anomaly_rate.anomalous_dimensions = 0; type_anomaly_rate.normal_dimensions = 0; } rrdset_done(host->type_anomaly_rate_rs); } /* * Detector Events */ { if (!host->detector_events_rs) { char id_buf[1024]; char name_buf[1024]; snprintfz(id_buf, 1024, "anomaly_detection_on_%s", localhost->machine_guid); snprintfz(name_buf, 1024, "anomaly_detection_on_%s", rrdhost_hostname(localhost)); host->detector_events_rs = rrdset_create( host->rh, "anomaly_detection", // type id_buf, // id name_buf, // name "anomaly_detection", // family "anomaly_detection.detector_events", // ctx "Anomaly detection events", // title "status", // units NETDATA_ML_PLUGIN, // plugin NETDATA_ML_MODULE_DETECTION, // module ML_CHART_PRIO_DETECTOR_EVENTS, // priority localhost->rrd_update_every, // update_every RRDSET_TYPE_LINE // chart_type ); rrdset_flag_set(host->detector_events_rs, RRDSET_FLAG_ANOMALY_DETECTION); host->detector_events_above_threshold_rd = rrddim_add(host->detector_events_rs, "above_threshold", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); host->detector_events_new_anomaly_event_rd = rrddim_add(host->detector_events_rs, "new_anomaly_event", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); } /* * Compute the values of the dimensions based on the host rate chart */ if (host->ml_running) { ONEWAYALLOC *OWA = onewayalloc_create(0); time_t Now = now_realtime_sec(); time_t Before = Now - host->rh->rrd_update_every; time_t After = Before - Cfg.anomaly_detection_query_duration; RRDR_OPTIONS Options = static_cast(0x00000000); RRDR *R = rrd2rrdr_legacy( OWA, host->anomaly_rate_rs, 1 /* points wanted */, After, Before, Cfg.anomaly_detection_grouping_method, 0 /* resampling time */, Options, "anomaly_rate", NULL /* group options */, 0, /* timeout */ 0, /* tier */ QUERY_SOURCE_ML, STORAGE_PRIORITY_SYNCHRONOUS ); if (R) { if (R->d == 1 && R->n == 1 && R->rows == 1) { static thread_local bool prev_above_threshold = false; bool above_threshold = R->v[0] >= Cfg.host_anomaly_rate_threshold; bool new_anomaly_event = above_threshold && !prev_above_threshold; prev_above_threshold = above_threshold; rrddim_set_by_pointer(host->detector_events_rs, host->detector_events_above_threshold_rd, above_threshold); rrddim_set_by_pointer(host->detector_events_rs, host->detector_events_new_anomaly_event_rd, new_anomaly_event); rrdset_done(host->detector_events_rs); } rrdr_free(OWA, R); } onewayalloc_destroy(OWA); } else { rrddim_set_by_pointer(host->detector_events_rs, host->detector_events_above_threshold_rd, 0); rrddim_set_by_pointer(host->detector_events_rs, host->detector_events_new_anomaly_event_rd, 0); rrdset_done(host->detector_events_rs); } } } void ml_update_training_statistics_chart(ml_training_thread_t *training_thread, const ml_training_stats_t &ts) { /* * queue stats */ { if (!training_thread->queue_stats_rs) { char id_buf[1024]; char name_buf[1024]; snprintfz(id_buf, 1024, "training_queue_%zu_stats", training_thread->id); snprintfz(name_buf, 1024, "training_queue_%zu_stats", training_thread->id); training_thread->queue_stats_rs = rrdset_create( localhost, "netdata", // type id_buf, // id name_buf, // name NETDATA_ML_CHART_FAMILY, // family "netdata.queue_stats", // ctx "Training queue stats", // title "items", // units NETDATA_ML_PLUGIN, // plugin NETDATA_ML_MODULE_TRAINING, // module NETDATA_ML_CHART_PRIO_QUEUE_STATS, // priority localhost->rrd_update_every, // update_every RRDSET_TYPE_LINE// chart_type ); rrdset_flag_set(training_thread->queue_stats_rs, RRDSET_FLAG_ANOMALY_DETECTION); training_thread->queue_stats_queue_size_rd = rrddim_add(training_thread->queue_stats_rs, "queue_size", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); training_thread->queue_stats_popped_items_rd = rrddim_add(training_thread->queue_stats_rs, "popped_items", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); } rrddim_set_by_pointer(training_thread->queue_stats_rs, training_thread->queue_stats_queue_size_rd, ts.queue_size); rrddim_set_by_pointer(training_thread->queue_stats_rs, training_thread->queue_stats_popped_items_rd, ts.num_popped_items); rrdset_done(training_thread->queue_stats_rs); } /* * training stats */ { if (!training_thread->training_time_stats_rs) { char id_buf[1024]; char name_buf[1024]; snprintfz(id_buf, 1024, "training_queue_%zu_time_stats", training_thread->id); snprintfz(name_buf, 1024, "training_queue_%zu_time_stats", training_thread->id); training_thread->training_time_stats_rs = rrdset_create( localhost, "netdata", // type id_buf, // id name_buf, // name NETDATA_ML_CHART_FAMILY, // family "netdata.training_time_stats", // ctx "Training time stats", // title "milliseconds", // units NETDATA_ML_PLUGIN, // plugin NETDATA_ML_MODULE_TRAINING, // module NETDATA_ML_CHART_PRIO_TRAINING_TIME_STATS, // priority localhost->rrd_update_every, // update_every RRDSET_TYPE_LINE// chart_type ); rrdset_flag_set(training_thread->training_time_stats_rs, RRDSET_FLAG_ANOMALY_DETECTION); training_thread->training_time_stats_allotted_rd = rrddim_add(training_thread->training_time_stats_rs, "allotted", NULL, 1, 1000, RRD_ALGORITHM_ABSOLUTE); training_thread->training_time_stats_consumed_rd = rrddim_add(training_thread->training_time_stats_rs, "consumed", NULL, 1, 1000, RRD_ALGORITHM_ABSOLUTE); training_thread->training_time_stats_remaining_rd = rrddim_add(training_thread->training_time_stats_rs, "remaining", NULL, 1, 1000, RRD_ALGORITHM_ABSOLUTE); } rrddim_set_by_pointer(training_thread->training_time_stats_rs, training_thread->training_time_stats_allotted_rd, ts.allotted_ut); rrddim_set_by_pointer(training_thread->training_time_stats_rs, training_thread->training_time_stats_consumed_rd, ts.consumed_ut); rrddim_set_by_pointer(training_thread->training_time_stats_rs, training_thread->training_time_stats_remaining_rd, ts.remaining_ut); rrdset_done(training_thread->training_time_stats_rs); } /* * training result stats */ { if (!training_thread->training_results_rs) { char id_buf[1024]; char name_buf[1024]; snprintfz(id_buf, 1024, "training_queue_%zu_results", training_thread->id); snprintfz(name_buf, 1024, "training_queue_%zu_results", training_thread->id); training_thread->training_results_rs = rrdset_create( localhost, "netdata", // type id_buf, // id name_buf, // name NETDATA_ML_CHART_FAMILY, // family "netdata.training_results", // ctx "Training results", // title "events", // units NETDATA_ML_PLUGIN, // plugin NETDATA_ML_MODULE_TRAINING, // module NETDATA_ML_CHART_PRIO_TRAINING_RESULTS, // priority localhost->rrd_update_every, // update_every RRDSET_TYPE_LINE// chart_type ); rrdset_flag_set(training_thread->training_results_rs, RRDSET_FLAG_ANOMALY_DETECTION); training_thread->training_results_ok_rd = rrddim_add(training_thread->training_results_rs, "ok", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); training_thread->training_results_invalid_query_time_range_rd = rrddim_add(training_thread->training_results_rs, "invalid-queries", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); training_thread->training_results_not_enough_collected_values_rd = rrddim_add(training_thread->training_results_rs, "not-enough-values", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); training_thread->training_results_null_acquired_dimension_rd = rrddim_add(training_thread->training_results_rs, "null-acquired-dimensions", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); training_thread->training_results_chart_under_replication_rd = rrddim_add(training_thread->training_results_rs, "chart-under-replication", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); } rrddim_set_by_pointer(training_thread->training_results_rs, training_thread->training_results_ok_rd, ts.training_result_ok); rrddim_set_by_pointer(training_thread->training_results_rs, training_thread->training_results_invalid_query_time_range_rd, ts.training_result_invalid_query_time_range); rrddim_set_by_pointer(training_thread->training_results_rs, training_thread->training_results_not_enough_collected_values_rd, ts.training_result_not_enough_collected_values); rrddim_set_by_pointer(training_thread->training_results_rs, training_thread->training_results_null_acquired_dimension_rd, ts.training_result_null_acquired_dimension); rrddim_set_by_pointer(training_thread->training_results_rs, training_thread->training_results_chart_under_replication_rd, ts.training_result_chart_under_replication); rrdset_done(training_thread->training_results_rs); } } void ml_update_global_statistics_charts(uint64_t models_consulted) { if (Cfg.enable_statistics_charts) { static RRDSET *st = NULL; static RRDDIM *rd = NULL; if (unlikely(!st)) { st = rrdset_create_localhost( "netdata" // type , "ml_models_consulted" // id , NULL // name , NETDATA_ML_CHART_FAMILY // family , NULL // context , "KMeans models used for prediction" // title , "models" // units , NETDATA_ML_PLUGIN // plugin , NETDATA_ML_MODULE_DETECTION // module , NETDATA_ML_CHART_PRIO_MACHINE_LEARNING_STATUS // priority , localhost->rrd_update_every // update_every , RRDSET_TYPE_AREA // chart_type ); rd = rrddim_add(st, "num_models_consulted", NULL, 1, 1, RRD_ALGORITHM_INCREMENTAL); } rrddim_set_by_pointer(st, rd, (collected_number) models_consulted); rrdset_done(st); } }