Revert "Refactor ML code and add support for multiple KMeans models. … (#14172)

19 files changed, 719 insertions, 1314 deletions

diff --git a/ml/ADCharts.cc b/ml/ADCharts.cc
index 49816f8f4b..00c593c0c4 100644
--- a/ml/ADCharts.cc
+++ b/ml/ADCharts.cc
@@ -3,182 +3,55 @@
 #include "ADCharts.h"
 #include "Config.h"
 
-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_for_" << localhost->machine_guid;
-            NameSS << "machine_learning_status_for_" << localhost->hostname;
-
-            MachineLearningStatusRS = rrdset_create_localhost(
-                "netdata", // type
-                IdSS.str().c_str(), // id
-                NameSS.str().c_str(), // name
-                "ml", // family
-                "netdata.machine_learning_status", // ctx
-                "Machine learning status", // title
-                "dimensions", // units
-                "netdata", // plugin
-                "ml", // 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);
-    }
+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;
 
-    /*
-     * 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_for_" << localhost->machine_guid;
-            NameSS << "metric_types_for_" << localhost->hostname;
-
-            MetricTypesRS = rrdset_create_localhost(
-                "netdata", // type
-                IdSS.str().c_str(), // id
-                NameSS.str().c_str(), // name
-                "ml", // family
-                "netdata.metric_types", // ctx
-                "Dimensions by metric type", // title
-                "dimensions", // units
-                "netdata", // plugin
-                "ml", // 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);
-    }
+        IdSS << "dimensions_on_" << localhost->machine_guid;
+        NameSS << "dimensions_on_" << localhost->hostname;
 
-    /*
-     * 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_for_" << localhost->machine_guid;
-            NameSS << "training_status_for_" << localhost->hostname;
-
-            TrainingStatusRS = rrdset_create_localhost(
-                "netdata", // type
-                IdSS.str().c_str(), // id
-                NameSS.str().c_str(), // name
-                "ml", // family
-                "netdata.training_status", // ctx
-                "Training status of dimensions", // title
-                "dimensions", // units
-                "netdata", // plugin
-                "ml", // 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);
+        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);
     }
 
-    /*
-     * 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_" << localhost->hostname;
-
-            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", // plugin
-                "ml", // 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);
-    }
+    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);
 
+    rrdset_done(RS);
 }
 
 void ml::updateHostAndDetectionRateCharts(RRDHOST *RH, collected_number AnomalyRate) {
@@ -202,7 +75,7 @@ void ml::updateHostAndDetectionRateCharts(RRDHOST *RH, collected_number AnomalyR
             "percentage", // units
             "netdata", // plugin
             "ml", // module
-            ML_CHART_PRIO_ANOMALY_RATE, // priority
+            39184, // priority
             RH->rrd_update_every, // update_every
             RRDSET_TYPE_LINE // chart_type
         );
@@ -236,7 +109,7 @@ void ml::updateHostAndDetectionRateCharts(RRDHOST *RH, collected_number AnomalyR
             "percentage", // units
             "netdata", // plugin
             "ml", // module
-            ML_CHART_PRIO_DETECTOR_EVENTS, // priority
+            39185, // priority
             RH->rrd_update_every, // update_every
             RRDSET_TYPE_LINE // chart_type
         );
@@ -270,7 +143,6 @@ void ml::updateHostAndDetectionRateCharts(RRDHOST *RH, collected_number AnomalyR
             0, /* tier */
             QUERY_SOURCE_ML
     );
-
     if(R) {
         assert(R->d == 1 && R->n == 1 && R->rows == 1);
 
@@ -285,227 +157,77 @@ void ml::updateHostAndDetectionRateCharts(RRDHOST *RH, collected_number AnomalyR
 
         rrdr_free(OWA, R);
     }
-
     onewayalloc_destroy(OWA);
 }
 
-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_" << localhost->machine_guid;
-            NameSS << "prediction_usage_for_" << localhost->hostname;
-
-            RS = rrdset_create_localhost(
-                "netdata", // type
-                IdSS.str().c_str(), // id
-                NameSS.str().c_str(), // name
-                "ml", // family
-                "netdata.prediction_usage", // ctx
-                "Prediction resource usage", // title
-                "milliseconds/s", // units
-                "netdata", // plugin
-                "ml", // 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);
-    }
+void ml::updateDetectionChart(RRDHOST *RH) {
+    static thread_local RRDSET *RS = nullptr;
+    static thread_local RRDDIM *UserRD, *SystemRD = nullptr;
 
-    /*
-     * 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_" << localhost->machine_guid;
-            NameSS << "training_usage_for_" << localhost->hostname;
-
-            RS = rrdset_create_localhost(
-                "netdata", // type
-                IdSS.str().c_str(), // id
-                NameSS.str().c_str(), // name
-                "ml", // family
-                "netdata.training_usage", // ctx
-                "Training resource usage", // title
-                "milliseconds/s", // units
-                "netdata", // plugin
-                "ml", // 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);
+    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);
     }
+
+    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);
 }
 
-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_for_" << localhost->machine_guid;
-            NameSS << "queue_stats_for_" << localhost->hostname;
-
-            RS = rrdset_create_localhost(
-                "netdata", // type
-                IdSS.str().c_str(), // id
-                NameSS.str().c_str(), // name
-                "ml", // family
-                "netdata.queue_stats", // ctx
-                "Training queue stats", // title
-                "items", // units
-                "netdata", // plugin
-                "ml", // 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);
-    }
+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;
 
-    /*
-     * 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_for_" << localhost->machine_guid;
-            NameSS << "training_time_stats_for_" << localhost->hostname;
-
-            RS = rrdset_create_localhost(
-                "netdata", // type
-                IdSS.str().c_str(), // id
-                NameSS.str().c_str(), // name
-                "ml", // family
-                "netdata.training_time_stats", // ctx
-                "Training time stats", // title
-                "milliseconds", // units
-                "netdata", // plugin
-                "ml", // 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);
-    }
+    if (!RS) {
+        std::stringstream IdSS, NameSS;
 
-    /*
-     * 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_for_" << localhost->machine_guid;
-            NameSS << "training_results_for_" << localhost->hostname;
-
-            RS = rrdset_create_localhost(
-                "netdata", // type
-                IdSS.str().c_str(), // id
-                NameSS.str().c_str(), // name
-                "ml", // family
-                "netdata.training_results", // ctx
-                "Training results", // title
-                "events", // units
-                "netdata", // plugin
-                "ml", // 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);
+        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
+        );
+
+        UserRD = rrddim_add(RS, "user", NULL, 1, 1000, RRD_ALGORITHM_INCREMENTAL);
+        SystemRD = rrddim_add(RS, "system", NULL, 1, 1000, RRD_ALGORITHM_INCREMENTAL);
     }
+
+    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);
 }
diff --git a/ml/ADCharts.h b/ml/ADCharts.h
index ee09669e22..0be324f7d7 100644
--- a/ml/ADCharts.h
+++ b/ml/ADCharts.h
@@ -3,18 +3,20 @@
 #ifndef ML_ADCHARTS_H
 #define ML_ADCHARTS_H
 
-#include "Stats.h"
 #include "ml-private.h"
 
 namespace ml {
 
-void updateDimensionsChart(RRDHOST *RH, const MachineLearningStats &MLS);
+void updateDimensionsChart(RRDHOST *RH,
+                           collected_number NumTrainedDimensions,
+                           collected_number NumNormalDimensions,
+                           collected_number NumAnomalousDimensions);
 
 void updateHostAndDetectionRateCharts(RRDHOST *RH, collected_number AnomalyRate);
 
-void updateResourceUsageCharts(RRDHOST *RH, const struct rusage &PredictionRU, const struct rusage &TrainingRU);
+void updateDetectionChart(RRDHOST *RH);
 
-void updateTrainingStatisticsChart(RRDHOST *RH, const TrainingStats &TS);
+void updateTrainingChart(RRDHOST *RH, struct rusage *TRU);
 
 } // namespace ml
 
diff --git a/ml/Chart.cc b/ml/Chart.cc
deleted file mode 100644
index e69de29bb2..0000000000
--- a/ml/Chart.cc
+++ /dev/null
diff --git a/ml/Chart.h b/ml/Chart.h
deleted file mode 100644
index c62f4bae38..0000000000
--- a/ml/Chart.h
+++ /dev/null
@@ -1,128 +0,0 @@
-// SPDX-License-Identifier: GPL-3.0-or-later
-
-#ifndef ML_CHART_H
-#define ML_CHART_H
-
-#include "Config.h"
-#include "Dimension.h"
-
-#include "ml-private.h"
-#include "json/single_include/nlohmann/json.hpp"
-
-namespace ml
-{
-
-class Chart {
-public:
-    Chart(RRDSET *RS) :
-        RS(RS),
-        MLS()
-     { }
-
-    RRDSET *getRS() const {
-        return RS;
-    }
-
-    bool isAvailableForML() {
-        return rrdset_is_available_for_exporting_and_alarms(RS);
-    }
-
-    void addDimension(Dimension *D) {
-        std::lock_guard<std::mutex> Lock(Mutex);
-        Dimensions[D->getRD()] = D;
-    }
-
-    void removeDimension(Dimension *D) {
-        std::lock_guard<std::mutex> Lock(Mutex);
-        Dimensions.erase(D->getRD());
-    }
-
-    void getModelsAsJson(nlohmann::json &Json) {
-        std::lock_guard<std::mutex> Lock(Mutex);
-
-        for (auto &DP : Dimensions) {
-            Dimension *D = DP.second;
-            nlohmann::json JsonArray = nlohmann::json::array();
-            for (const KMeans &KM : D->getModels()) {
-                nlohmann::json J;
-                KM.toJson(J);
-                JsonArray.push_back(J);
-            }
-
-            Json[getMLDimensionID(D->getRD())] = JsonArray;
-        }
-    }
-
-    void updateBegin() {
-        Mutex.lock();
-        MLS = {};
-    }
-
-    void updateDimension(Dimension *D, bool IsAnomalous) {
-        switch (D->getMLS()) {
-            case MachineLearningStatus::DisabledDueToUniqueUpdateEvery:
-                MLS.NumMachineLearningStatusDisabledUE++;
-                return;
-            case MachineLearningStatus::DisabledDueToExcludedChart:
-                MLS.NumMachineLearningStatusDisabledSP++;
-                return;
-            case MachineLearningStatus::Enabled: {
-                MLS.NumMachineLearningStatusEnabled++;
-
-                switch (D->getMT()) {
-                    case MetricType::Constant:
-                        MLS.NumMetricTypeConstant++;
-                        MLS.NumTrainingStatusTrained++;
-                        MLS.NumNormalDimensions++;
-                        return;
-                    case MetricType::Variable:
-                        MLS.NumMetricTypeVariable++;
-                        break;
-                }
-
-                switch (D->getTS()) {
-                    case TrainingStatus::Untrained:
-                        MLS.NumTrainingStatusUntrained++;
-                        return;
-                    case TrainingStatus::PendingWithoutModel:
-                        MLS.NumTrainingStatusPendingWithoutModel++;
-                        return;
-                    case TrainingStatus::Trained:
-                        MLS.NumTrainingStatusTrained++;
-
-                        MLS.NumAnomalousDimensions += IsAnomalous;
-                        MLS.NumNormalDimensions += !IsAnomalous;
-                        return;
-                    case TrainingStatus::PendingWithModel:
-                        MLS.NumTrainingStatusPendingWithModel++;
-
-                        MLS.NumAnomalousDimensions += IsAnomalous;
-                        MLS.NumNormalDimensions += !IsAnomalous;
-                        return;
-                }
-
-                return;
-            }
-        }
-    }
-
-    void updateEnd() {
-        Mutex.unlock();
-    }
-
-    MachineLearningStats getMLS() {
-        std::lock_guard<std::mutex> Lock(Mutex);
-        return MLS;
-    }
-
-private:
-    RRDSET *RS;
-    MachineLearningStats MLS;
-
-    std::mutex Mutex;
-    std::unordered_map<RRDDIM *, Dimension *> Dimensions;
-};
-
-} // namespace ml
-
-#endif /* ML_CHART_H */
diff --git a/ml/Config.cc b/ml/Config.cc
index ba3a614452..eedd8c29fd 100644
--- a/ml/Config.cc
+++ b/ml/Config.cc
@@ -31,7 +31,7 @@ void Config::readMLConfig(void) {
     unsigned MaxTrainSamples = config_get_number(ConfigSectionML, "maximum num samples to train", 4 * 3600);
     unsigned MinTrainSamples = config_get_number(ConfigSectionML, "minimum num samples to train", 1 * 900);
     unsigned TrainEvery = config_get_number(ConfigSectionML, "train every", 1 * 3600);
-    unsigned NumModelsToUse = config_get_number(ConfigSectionML, "number of models per dimension", 1);
+    unsigned NumModelsToUse = config_get_number(ConfigSectionML, "number of models per dimension", 1 * 24);
 
     unsigned DiffN = config_get_number(ConfigSectionML, "num samples to diff", 1);
     unsigned SmoothN = config_get_number(ConfigSectionML, "num samples to smooth", 3);
@@ -53,7 +53,7 @@ void Config::readMLConfig(void) {
     MaxTrainSamples = clamp<unsigned>(MaxTrainSamples, 1 * 3600, 24 * 3600);
     MinTrainSamples = clamp<unsigned>(MinTrainSamples, 1 * 900, 6 * 3600);
     TrainEvery = clamp<unsigned>(TrainEvery, 1 * 3600, 6 * 3600);
-    NumModelsToUse = clamp<unsigned>(NumModelsToUse, 1, 7 * 24);
+    NumModelsToUse = clamp<unsigned>(TrainEvery, 1, 7 * 24);
 
     DiffN = clamp(DiffN, 0u, 1u);
     SmoothN = clamp(SmoothN, 0u, 5u);
@@ -108,7 +108,7 @@ void Config::readMLConfig(void) {
     // Always exclude anomaly_detection charts from training.
     Cfg.ChartsToSkip = "anomaly_detection.* ";
     Cfg.ChartsToSkip += config_get(ConfigSectionML, "charts to skip from training", "netdata.*");
-    Cfg.SP_ChartsToSkip = simple_pattern_create(Cfg.ChartsToSkip.c_str(), NULL, SIMPLE_PATTERN_EXACT);
+    Cfg.SP_ChartsToSkip = simple_pattern_create(ChartsToSkip.c_str(), NULL, SIMPLE_PATTERN_EXACT);
 
     Cfg.StreamADCharts = config_get_boolean(ConfigSectionML, "stream anomaly detection charts", true);
 }
diff --git a/ml/Config.h b/ml/Config.h
index f10e114926..d876d4aa41 100644
--- a/ml/Config.h
+++ b/ml/Config.h
@@ -14,7 +14,6 @@ public:
     unsigned MaxTrainSamples;
     unsigned MinTrainSamples;
     unsigned TrainEvery;
-
     unsigned NumModelsToUse;
 
     unsigned DBEngineAnomalyRateEvery;
diff --git a/ml/Dimension.cc b/ml/Dimension.cc
index c2195f175d..bf34abb72f 100644
--- a/ml/Dimension.cc
+++ b/ml/Dimension.cc
@@ -3,174 +3,84 @@
 #include "Config.h"
 #include "Dimension.h"
 #include "Query.h"
-#include "Host.h"
 
 using namespace ml;
 
-static const char *mls2str(MachineLearningStatus MLS) {
-    switch (MLS) {
-        case ml::MachineLearningStatus::Enabled:
-            return "enabled";
-        case ml::MachineLearningStatus::DisabledDueToUniqueUpdateEvery:
-            return "disabled-ue";
-        case ml::MachineLearningStatus::DisabledDueToExcludedChart:
-            return "disabled-sp";
-        default:
-            return "unknown";
-    }
-}
-
-static const char *mt2str(MetricType MT) {
-    switch (MT) {
-        case ml::MetricType::Constant:
-            ret