diff options
Diffstat (limited to 'ml/ADCharts.cc')
| -rw-r--r-- | ml/ADCharts.cc | 543 |
1 files changed, 414 insertions, 129 deletions
diff --git a/ml/ADCharts.cc b/ml/ADCharts.cc index 00c593c0..cbb13f5d 100644 --- a/ml/ADCharts.cc +++ b/ml/ADCharts.cc @@ -3,55 +3,185 @@ #include "ADCharts.h" #include "Config.h" -void ml::updateDimensionsChart(RRDHOST *RH, - collected_number NumTrainedDimensions, - collected_number NumNormalDimensions, - collected_number NumAnomalousDimensions) { - static thread_local RRDSET *RS = nullptr; - static thread_local RRDDIM *NumTotalDimensionsRD = nullptr; - static thread_local RRDDIM *NumTrainedDimensionsRD = nullptr; - static thread_local RRDDIM *NumNormalDimensionsRD = nullptr; - static thread_local RRDDIM *NumAnomalousDimensionsRD = nullptr; - - if (!RS) { - std::stringstream IdSS, NameSS; +void ml::updateDimensionsChart(RRDHOST *RH, const MachineLearningStats &MLS) { + /* + * Machine learning status + */ + { + static thread_local RRDSET *MachineLearningStatusRS = nullptr; + + static thread_local RRDDIM *Enabled = nullptr; + static thread_local RRDDIM *DisabledUE = nullptr; + static thread_local RRDDIM *DisabledSP = nullptr; + + if (!MachineLearningStatusRS) { + std::stringstream IdSS, NameSS; + + IdSS << "machine_learning_status_on_" << localhost->machine_guid; + NameSS << "machine_learning_status_on_" << rrdhost_hostname(localhost); + + MachineLearningStatusRS = rrdset_create( + RH, + "netdata", // type + IdSS.str().c_str(), // id + NameSS.str().c_str(), // 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 + RH->rrd_update_every, // update_every + RRDSET_TYPE_LINE // chart_type + ); + rrdset_flag_set(MachineLearningStatusRS , RRDSET_FLAG_ANOMALY_DETECTION); + + Enabled = rrddim_add(MachineLearningStatusRS, "enabled", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + DisabledUE = rrddim_add(MachineLearningStatusRS, "disabled-ue", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + DisabledSP = rrddim_add(MachineLearningStatusRS, "disabled-sp", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + } + + rrddim_set_by_pointer(MachineLearningStatusRS, Enabled, MLS.NumMachineLearningStatusEnabled); + rrddim_set_by_pointer(MachineLearningStatusRS, DisabledUE, MLS.NumMachineLearningStatusDisabledUE); + rrddim_set_by_pointer(MachineLearningStatusRS, DisabledSP, MLS.NumMachineLearningStatusDisabledSP); + + rrdset_done(MachineLearningStatusRS); + } - IdSS << "dimensions_on_" << localhost->machine_guid; - NameSS << "dimensions_on_" << localhost->hostname; + /* + * Metric type + */ + { + static thread_local RRDSET *MetricTypesRS = nullptr; + + static thread_local RRDDIM *Constant = nullptr; + static thread_local RRDDIM *Variable = nullptr; + + if (!MetricTypesRS) { + std::stringstream IdSS, NameSS; + + IdSS << "metric_types_on_" << localhost->machine_guid; + NameSS << "metric_types_on_" << rrdhost_hostname(localhost); + + MetricTypesRS = rrdset_create( + RH, + "netdata", // type + IdSS.str().c_str(), // id + NameSS.str().c_str(), // 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 + RH->rrd_update_every, // update_every + RRDSET_TYPE_LINE // chart_type + ); + rrdset_flag_set(MetricTypesRS, RRDSET_FLAG_ANOMALY_DETECTION); + + Constant = rrddim_add(MetricTypesRS, "constant", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + Variable = rrddim_add(MetricTypesRS, "variable", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + } + + rrddim_set_by_pointer(MetricTypesRS, Constant, MLS.NumMetricTypeConstant); + rrddim_set_by_pointer(MetricTypesRS, Variable, MLS.NumMetricTypeVariable); + + rrdset_done(MetricTypesRS); + } - RS = rrdset_create( - RH, - "anomaly_detection", // type - IdSS.str().c_str(), // id - NameSS.str().c_str(), // name - "dimensions", // family - "anomaly_detection.dimensions", // ctx - "Anomaly detection dimensions", // title - "dimensions", // units - "netdata", // plugin - "ml", // module - 39183, // priority - RH->rrd_update_every, // update_every - RRDSET_TYPE_LINE // chart_type - ); - rrdset_flag_set(RS, RRDSET_FLAG_ANOMALY_DETECTION); - - NumTotalDimensionsRD = rrddim_add(RS, "total", NULL, - 1, 1, RRD_ALGORITHM_ABSOLUTE); - NumTrainedDimensionsRD = rrddim_add(RS, "trained", NULL, - 1, 1, RRD_ALGORITHM_ABSOLUTE); - NumNormalDimensionsRD = rrddim_add(RS, "normal", NULL, - 1, 1, RRD_ALGORITHM_ABSOLUTE); - NumAnomalousDimensionsRD = rrddim_add(RS, "anomalous", NULL, - 1, 1, RRD_ALGORITHM_ABSOLUTE); + /* + * Training status + */ + { + static thread_local RRDSET *TrainingStatusRS = nullptr; + + static thread_local RRDDIM *Untrained = nullptr; + static thread_local RRDDIM *PendingWithoutModel = nullptr; + static thread_local RRDDIM *Trained = nullptr; + static thread_local RRDDIM *PendingWithModel = nullptr; + + if (!TrainingStatusRS) { + std::stringstream IdSS, NameSS; + + IdSS << "training_status_on_" << localhost->machine_guid; + NameSS << "training_status_on_" << rrdhost_hostname(localhost); + + TrainingStatusRS = rrdset_create( + RH, + "netdata", // type + IdSS.str().c_str(), // id + NameSS.str().c_str(), // 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 + RH->rrd_update_every, // update_every + RRDSET_TYPE_LINE // chart_type + ); + + rrdset_flag_set(TrainingStatusRS, RRDSET_FLAG_ANOMALY_DETECTION); + + Untrained = rrddim_add(TrainingStatusRS, "untrained", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + PendingWithoutModel = rrddim_add(TrainingStatusRS, "pending-without-model", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + Trained = rrddim_add(TrainingStatusRS, "trained", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + PendingWithModel = rrddim_add(TrainingStatusRS, "pending-with-model", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + } + + rrddim_set_by_pointer(TrainingStatusRS, Untrained, MLS.NumTrainingStatusUntrained); + rrddim_set_by_pointer(TrainingStatusRS, PendingWithoutModel, MLS.NumTrainingStatusPendingWithoutModel); + rrddim_set_by_pointer(TrainingStatusRS, Trained, MLS.NumTrainingStatusTrained); + rrddim_set_by_pointer(TrainingStatusRS, PendingWithModel, MLS.NumTrainingStatusPendingWithModel); + + rrdset_done(TrainingStatusRS); } - rrddim_set_by_pointer(RS, NumTotalDimensionsRD, NumNormalDimensions + NumAnomalousDimensions); - rrddim_set_by_pointer(RS, NumTrainedDimensionsRD, NumTrainedDimensions); - rrddim_set_by_pointer(RS, NumNormalDimensionsRD, NumNormalDimensions); - rrddim_set_by_pointer(RS, NumAnomalousDimensionsRD, NumAnomalousDimensions); + /* + * Prediction status + */ + { + static thread_local RRDSET *PredictionRS = nullptr; + + static thread_local RRDDIM *Anomalous = nullptr; + static thread_local RRDDIM *Normal = nullptr; + + if (!PredictionRS) { + std::stringstream IdSS, NameSS; + + IdSS << "dimensions_on_" << localhost->machine_guid; + NameSS << "dimensions_on_" << rrdhost_hostname(localhost); + + PredictionRS = rrdset_create( + RH, + "anomaly_detection", // type + IdSS.str().c_str(), // id + NameSS.str().c_str(), // 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 + RH->rrd_update_every, // update_every + RRDSET_TYPE_LINE // chart_type + ); + rrdset_flag_set(PredictionRS, RRDSET_FLAG_ANOMALY_DETECTION); + + Anomalous = rrddim_add(PredictionRS, "anomalous", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + Normal = rrddim_add(PredictionRS, "normal", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + } + + rrddim_set_by_pointer(PredictionRS, Anomalous, MLS.NumAnomalousDimensions); + rrddim_set_by_pointer(PredictionRS, Normal, MLS.NumNormalDimensions); + + rrdset_done(PredictionRS); + } - rrdset_done(RS); } void ml::updateHostAndDetectionRateCharts(RRDHOST *RH, collected_number AnomalyRate) { @@ -62,20 +192,20 @@ void ml::updateHostAndDetectionRateCharts(RRDHOST *RH, collected_number AnomalyR std::stringstream IdSS, NameSS; IdSS << "anomaly_rate_on_" << localhost->machine_guid; - NameSS << "anomaly_rate_on_" << localhost->hostname; + NameSS << "anomaly_rate_on_" << rrdhost_hostname(localhost); HostRateRS = rrdset_create( - RH, - "anomaly_detection", // type + RH, + "anomaly_detection", // type IdSS.str().c_str(), // id NameSS.str().c_str(), // name "anomaly_rate", // family "anomaly_detection.anomaly_rate", // ctx "Percentage of anomalous dimensions", // title "percentage", // units - "netdata", // plugin - "ml", // module - 39184, // priority + NETDATA_ML_PLUGIN, // plugin + NETDATA_ML_MODULE_DETECTION, // module + ML_CHART_PRIO_ANOMALY_RATE, // priority RH->rrd_update_every, // update_every RRDSET_TYPE_LINE // chart_type ); @@ -96,20 +226,20 @@ void ml::updateHostAndDetectionRateCharts(RRDHOST *RH, collected_number AnomalyR std::stringstream IdSS, NameSS; IdSS << "anomaly_detection_on_" << localhost->machine_guid; - NameSS << "anomaly_detection_on_" << localhost->hostname; + NameSS << "anomaly_detection_on_" << rrdhost_hostname(localhost); AnomalyDetectionRS = rrdset_create( - RH, - "anomaly_detection", // type + RH, + "anomaly_detection", // type IdSS.str().c_str(), // id NameSS.str().c_str(), // name "anomaly_detection", // family "anomaly_detection.detector_events", // ctx "Anomaly detection events", // title "percentage", // units - "netdata", // plugin - "ml", // module - 39185, // priority + NETDATA_ML_PLUGIN, // plugin + NETDATA_ML_MODULE_DETECTION, // module + ML_CHART_PRIO_DETECTOR_EVENTS, // priority RH->rrd_update_every, // update_every RRDSET_TYPE_LINE // chart_type ); @@ -141,93 +271,248 @@ void ml::updateHostAndDetectionRateCharts(RRDHOST *RH, collected_number AnomalyR NULL /* group options */, 0, /* timeout */ 0, /* tier */ - QUERY_SOURCE_ML + QUERY_SOURCE_ML, + STORAGE_PRIORITY_BEST_EFFORT ); - if(R) { - assert(R->d == 1 && R->n == 1 && R->rows == 1); - static thread_local bool PrevAboveThreshold = false; - bool AboveThreshold = R->v[0] >= Cfg.HostAnomalyRateThreshold; - bool NewAnomalyEvent = AboveThreshold && !PrevAboveThreshold; - PrevAboveThreshold = AboveThreshold; + if(R) { + if(R->d == 1 && R->n == 1 && R->rows == 1) { + static thread_local bool PrevAboveThreshold = false; + bool AboveThreshold = R->v[0] >= Cfg.HostAnomalyRateThreshold; + bool NewAnomalyEvent = AboveThreshold && !PrevAboveThreshold; + PrevAboveThreshold = AboveThreshold; - rrddim_set_by_pointer(AnomalyDetectionRS, AboveThresholdRD, AboveThreshold); - rrddim_set_by_pointer(AnomalyDetectionRS, NewAnomalyEventRD, NewAnomalyEvent); - rrdset_done(AnomalyDetectionRS); + rrddim_set_by_pointer(AnomalyDetectionRS, AboveThresholdRD, AboveThreshold); + rrddim_set_by_pointer(AnomalyDetectionRS, NewAnomalyEventRD, NewAnomalyEvent); + rrdset_done(AnomalyDetectionRS); + } rrdr_free(OWA, R); } + onewayalloc_destroy(OWA); } -void ml::updateDetectionChart(RRDHOST *RH) { - static thread_local RRDSET *RS = nullptr; - static thread_local RRDDIM *UserRD, *SystemRD = nullptr; - - if (!RS) { - std::stringstream IdSS, NameSS; - - IdSS << "prediction_stats_" << RH->machine_guid; - NameSS << "prediction_stats_for_" << RH->hostname; - - RS = rrdset_create_localhost( - "netdata", // type - IdSS.str().c_str(), // id - NameSS.str().c_str(), // name - "ml", // family - "netdata.prediction_stats", // ctx - "Prediction thread CPU usage", // title - "milliseconds/s", // units - "netdata", // plugin - "ml", // module - 136000, // priority - RH->rrd_update_every, // update_every - RRDSET_TYPE_STACKED // chart_type - ); - - UserRD = rrddim_add(RS, "user", NULL, 1, 1000, RRD_ALGORITHM_INCREMENTAL); - SystemRD = rrddim_add(RS, "system", NULL, 1, 1000, RRD_ALGORITHM_INCREMENTAL); +void ml::updateResourceUsageCharts(RRDHOST *RH, const struct rusage &PredictionRU, const struct rusage &TrainingRU) { + /* + * prediction rusage + */ + { + static thread_local RRDSET *RS = nullptr; + + static thread_local RRDDIM *User = nullptr; + static thread_local RRDDIM *System = nullptr; + + if (!RS) { + std::stringstream IdSS, NameSS; + + IdSS << "prediction_usage_for_" << RH->machine_guid; + NameSS << "prediction_usage_for_" << rrdhost_hostname(RH); + + RS = rrdset_create_localhost( + "netdata", // type + IdSS.str().c_str(), // id + NameSS.str().c_str(), // name + NETDATA_ML_CHART_FAMILY, // family + "netdata.prediction_usage", // ctx + "Prediction resource usage", // title + "milliseconds/s", // units + NETDATA_ML_PLUGIN, // plugin + NETDATA_ML_MODULE_PREDICTION, // module + NETDATA_ML_CHART_PRIO_PREDICTION_USAGE, // priority + RH->rrd_update_every, // update_every + RRDSET_TYPE_STACKED // chart_type + ); + rrdset_flag_set(RS, RRDSET_FLAG_ANOMALY_DETECTION); + + User = rrddim_add(RS, "user", NULL, 1, 1000, RRD_ALGORITHM_INCREMENTAL); + System = rrddim_add(RS, "system", NULL, 1, 1000, RRD_ALGORITHM_INCREMENTAL); + } + + rrddim_set_by_pointer(RS, User, PredictionRU.ru_utime.tv_sec * 1000000ULL + PredictionRU.ru_utime.tv_usec); + rrddim_set_by_pointer(RS, System, PredictionRU.ru_stime.tv_sec * 1000000ULL + PredictionRU.ru_stime.tv_usec); + + rrdset_done(RS); } - struct rusage TRU; - getrusage(RUSAGE_THREAD, &TRU); - - rrddim_set_by_pointer(RS, UserRD, TRU.ru_utime.tv_sec * 1000000ULL + TRU.ru_utime.tv_usec); - rrddim_set_by_pointer(RS, SystemRD, TRU.ru_stime.tv_sec * 1000000ULL + TRU.ru_stime.tv_usec); - rrdset_done(RS); + /* + * training rusage + */ + { + static thread_local RRDSET *RS = nullptr; + + static thread_local RRDDIM *User = nullptr; + static thread_local RRDDIM *System = nullptr; + + if (!RS) { + std::stringstream IdSS, NameSS; + + IdSS << "training_usage_for_" << RH->machine_guid; + NameSS << "training_usage_for_" << rrdhost_hostname(RH); + + RS = rrdset_create_localhost( + "netdata", // type + IdSS.str().c_str(), // id + NameSS.str().c_str(), // name + NETDATA_ML_CHART_FAMILY, // family + "netdata.training_usage", // ctx + "Training resource usage", // title + "milliseconds/s", // units + NETDATA_ML_PLUGIN, // plugin + NETDATA_ML_MODULE_TRAINING, // module + NETDATA_ML_CHART_PRIO_TRAINING_USAGE, // priority + RH->rrd_update_every, // update_every + RRDSET_TYPE_STACKED // chart_type + ); + rrdset_flag_set(RS, RRDSET_FLAG_ANOMALY_DETECTION); + + User = rrddim_add(RS, "user", NULL, 1, 1000, RRD_ALGORITHM_INCREMENTAL); + System = rrddim_add(RS, "system", NULL, 1, 1000, RRD_ALGORITHM_INCREMENTAL); + } + + rrddim_set_by_pointer(RS, User, TrainingRU.ru_utime.tv_sec * 1000000ULL + TrainingRU.ru_utime.tv_usec); + rrddim_set_by_pointer(RS, System, TrainingRU.ru_stime.tv_sec * 1000000ULL + TrainingRU.ru_stime.tv_usec); + + rrdset_done(RS); + } } -void ml::updateTrainingChart(RRDHOST *RH, struct rusage *TRU) { - static thread_local RRDSET *RS = nullptr; - static thread_local RRDDIM *UserRD = nullptr; - static thread_local RRDDIM *SystemRD = nullptr; - - if (!RS) { - std::stringstream IdSS, NameSS; - - IdSS << "training_stats_" << RH->machine_guid; - NameSS << "training_stats_for_" << RH->hostname; - - RS = rrdset_create_localhost( - "netdata", // type - IdSS.str().c_str(), // id - NameSS.str().c_str(), // name - "ml", // family - "netdata.training_stats", // ctx - "Training thread CPU usage", // title - "milliseconds/s", // units - "netdata", // plugin - "ml", // module - 136001, // priority - RH->rrd_update_every, // update_every - RRDSET_TYPE_STACKED // chart_type - ); +void ml::updateTrainingStatisticsChart(RRDHOST *RH, const TrainingStats &TS) { + /* + * queue stats + */ + { + static thread_local RRDSET *RS = nullptr; + + static thread_local RRDDIM *QueueSize = nullptr; + static thread_local RRDDIM *PoppedItems = nullptr; + + if (!RS) { + std::stringstream IdSS, NameSS; + + IdSS << "queue_stats_on_" << localhost->machine_guid; + NameSS << "queue_stats_on_" << rrdhost_hostname(localhost); + + RS = rrdset_create( + RH, + "netdata", // type + IdSS.str().c_str(), // id + NameSS.str().c_str(), // 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 + RH->rrd_update_every, // update_every + RRDSET_TYPE_LINE// chart_type + ); + rrdset_flag_set(RS, RRDSET_FLAG_ANOMALY_DETECTION); + + QueueSize = rrddim_add(RS, "queue_size", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + PoppedItems = rrddim_add(RS, "popped_items", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + } + + rrddim_set_by_pointer(RS, QueueSize, TS.QueueSize); + rrddim_set_by_pointer(RS, PoppedItems, TS.NumPoppedItems); + + rrdset_done(RS); + } - UserRD = rrddim_add(RS, "user", NULL, 1, 1000, RRD_ALGORITHM_INCREMENTAL); - SystemRD = rrddim_add(RS, "system", NULL, 1, 1000, RRD_ALGORITHM_INCREMENTAL); + /* + * training stats + */ + { + static thread_local RRDSET *RS = nullptr; + + static thread_local RRDDIM *Allotted = nullptr; + static thread_local RRDDIM *Consumed = nullptr; + static thread_local RRDDIM *Remaining = nullptr; + + if (!RS) { + std::stringstream IdSS, NameSS; + + IdSS << "training_time_stats_on_" << localhost->machine_guid; + NameSS << "training_time_stats_on_" << rrdhost_hostname(localhost); + + RS = rrdset_create( + RH, + "netdata", // type + IdSS.str().c_str(), // id + NameSS.str().c_str(), // 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 + RH->rrd_update_every, // update_every + RRDSET_TYPE_LINE// chart_type + ); + rrdset_flag_set(RS, RRDSET_FLAG_ANOMALY_DETECTION); + + Allotted = rrddim_add(RS, "allotted", NULL, 1, 1000, RRD_ALGORITHM_ABSOLUTE); + Consumed = rrddim_add(RS, "consumed", NULL, 1, 1000, RRD_ALGORITHM_ABSOLUTE); + Remaining = rrddim_add(RS, "remaining", NULL, 1, 1000, RRD_ALGORITHM_ABSOLUTE); + } + + rrddim_set_by_pointer(RS, Allotted, TS.AllottedUT); + rrddim_set_by_pointer(RS, Consumed, TS.ConsumedUT); + rrddim_set_by_pointer(RS, Remaining, TS.RemainingUT); + + rrdset_done(RS); } - rrddim_set_by_pointer(RS, UserRD, TRU->ru_utime.tv_sec * 1000000ULL + TRU->ru_utime.tv_usec); - rrddim_set_by_pointer(RS, SystemRD, TRU->ru_stime.tv_sec * 1000000ULL + TRU->ru_stime.tv_usec); - rrdset_done(RS); + /* + * training result stats + */ + { + static thread_local RRDSET *RS = nullptr; + + static thread_local RRDDIM *Ok = nullptr; + static thread_local RRDDIM *InvalidQueryTimeRange = nullptr; + static thread_local RRDDIM *NotEnoughCollectedValues = nullptr; + static thread_local RRDDIM *NullAcquiredDimension = nullptr; + static thread_local RRDDIM *ChartUnderReplication = nullptr; + + if (!RS) { + std::stringstream IdSS, NameSS; + + IdSS << "training_results_on_" << localhost->machine_guid; + NameSS << "training_results_on_" << rrdhost_hostname(localhost); + + RS = rrdset_create( + RH, + "netdata", // type + IdSS.str().c_str(), // id + NameSS.str().c_str(), // 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 + RH->rrd_update_every, // update_every + RRDSET_TYPE_LINE// chart_type + ); + rrdset_flag_set(RS, RRDSET_FLAG_ANOMALY_DETECTION); + + Ok = rrddim_add(RS, "ok", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + InvalidQueryTimeRange = rrddim_add(RS, "invalid-queries", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + NotEnoughCollectedValues = rrddim_add(RS, "not-enough-values", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + NullAcquiredDimension = rrddim_add(RS, "null-acquired-dimensions", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + ChartUnderReplication = rrddim_add(RS, "chart-under-replication", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + } + + rrddim_set_by_pointer(RS, Ok, TS.TrainingResultOk); + rrddim_set_by_pointer(RS, InvalidQueryTimeRange, TS.TrainingResultInvalidQueryTimeRange); + rrddim_set_by_pointer(RS, NotEnoughCollectedValues, TS.TrainingResultNotEnoughCollectedValues); + rrddim_set_by_pointer(RS, NullAcquiredDimension, TS.TrainingResultNullAcquiredDimension); + rrddim_set_by_pointer(RS, ChartUnderReplication, TS.TrainingResultChartUnderReplication); + + rrdset_done(RS); + } } |