diff options
Diffstat (limited to 'ml/ad_charts.cc')
| -rw-r--r-- | ml/ad_charts.cc | 475 |
1 files changed, 475 insertions, 0 deletions
diff --git a/ml/ad_charts.cc b/ml/ad_charts.cc new file mode 100644 index 00000000..086cd5aa --- /dev/null +++ b/ml/ad_charts.cc @@ -0,0 +1,475 @@ +// 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); + } + + 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); + + 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); + } +} + +void ml_update_host_and_detection_rate_charts(ml_host_t *host, collected_number AnomalyRate) { + /* + * 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); + } + + /* + * 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 + "percentage", // 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 + */ + 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<RRDR_OPTIONS>(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); + } +} + +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); + } +} |