Merging upstream version 1.41.0.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

6 files changed, 235 insertions, 75 deletions

diff --git a/ml/Config.cc b/ml/Config.cc
index c00d2e8ee..f733bcf7d 100644
--- a/ml/Config.cc
+++ b/ml/Config.cc
@@ -83,7 +83,7 @@ void ml_config_load(ml_config_t *cfg) {
      */
 
     if (min_train_samples >= max_train_samples) {
-        error("invalid min/max train samples found (%u >= %u)", min_train_samples, max_train_samples);
+        netdata_log_error("invalid min/max train samples found (%u >= %u)", min_train_samples, max_train_samples);
 
         min_train_samples = 1 * 3600;
         max_train_samples = 6 * 3600;
diff --git a/ml/ad_charts.cc b/ml/ad_charts.cc
index bd065cfcc..ca4dca139 100644
--- a/ml/ad_charts.cc
+++ b/ml/ad_charts.cc
@@ -183,6 +183,41 @@ void ml_update_dimensions_chart(ml_host_t *host, const ml_machine_learning_stats
 
         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) {
@@ -260,47 +295,55 @@ void ml_update_host_and_detection_rate_charts(ml_host_t *host, collected_number
         /*
          * 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);
+        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<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);
             }
 
-            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);
         }
-
-        onewayalloc_destroy(OWA);
     }
 }
 
diff --git a/ml/ml-dummy.c b/ml/ml-dummy.c
index 708ab68ea..2ad6cc726 100644
--- a/ml/ml-dummy.c
+++ b/ml/ml-dummy.c
@@ -33,6 +33,14 @@ void ml_host_delete(RRDHOST *rh) {
     UNUSED(rh);
 }
 
+void ml_host_start(RRDHOST *rh) {
+    UNUSED(rh);
+}
+
+void ml_host_stop(RRDHOST *rh) {
+    UNUSED(rh);
+}
+
 void ml_host_start_training_thread(RRDHOST *rh) {
     UNUSED(rh);
 }
@@ -101,4 +109,13 @@ void ml_update_global_statistics_charts(uint64_t models_consulted) {
     UNUSED(models_consulted);
 }
 
+bool ml_host_get_host_status(RRDHOST *rh, struct ml_metrics_statistics *mlm) {
+    memset(mlm, 0, sizeof(*mlm));
+    return false;
+}
+
+bool ml_host_running(RRDHOST *rh) {
+    return false;
+}
+
 #endif
diff --git a/ml/ml-private.h b/ml/ml-private.h
index 2ed70d1ca..f0e2e7eaf 100644
--- a/ml/ml-private.h
+++ b/ml/ml-private.h
@@ -195,7 +195,7 @@ typedef struct {
     std::vector<calculated_number_t> cns;
 
     std::vector<ml_kmeans_t> km_contexts;
-    netdata_mutex_t mutex;
+    SPINLOCK slock;
     ml_kmeans_t kmeans;
     std::vector<DSample> feature;
 
@@ -206,8 +206,6 @@ typedef struct {
 typedef struct {
     RRDSET *rs;
     ml_machine_learning_stats_t mls;
-
-    netdata_mutex_t mutex;
 } ml_chart_t;
 
 void ml_chart_update_dimension(ml_chart_t *chart, ml_dimension_t *dim, bool is_anomalous);
@@ -215,6 +213,8 @@ void ml_chart_update_dimension(ml_chart_t *chart, ml_dimension_t *dim, bool is_a
 typedef struct {
     RRDHOST *rh;
 
+    std::atomic<bool> ml_running;
+
     ml_machine_learning_stats_t mls;
 
     calculated_number_t host_anomaly_rate;
@@ -227,6 +227,9 @@ typedef struct {
      * bookkeeping for anomaly detection charts
     */
 
+    RRDSET *ml_running_rs;
+    RRDDIM *ml_running_rd;
+
     RRDSET *machine_learning_status_rs;
     RRDDIM *machine_learning_status_enabled_rd;
     RRDDIM *machine_learning_status_disabled_sp_rd;
diff --git a/ml/ml.cc b/ml/ml.cc
index a5f0fa062..396967492 100644
--- a/ml/ml.cc
+++ b/ml/ml.cc
@@ -337,7 +337,7 @@ ml_dimension_calculated_numbers(ml_training_thread_t *training_thread, ml_dimens
     // Figure out what our time window should be.
     training_response.query_before_t = training_response.last_entry_on_response;
     training_response.query_after_t = std::max(
-        training_response.query_before_t - static_cast<time_t>((max_n - 1) * dim->rd->update_every),
+        training_response.query_before_t - static_cast<time_t>((max_n - 1) * dim->rd->rrdset->update_every),
         training_response.first_entry_on_response
     );
 
@@ -568,9 +568,9 @@ int ml_dimension_load_models(RRDDIM *rd) {
     if (!dim)
         return 0;
 
-    netdata_mutex_lock(&dim->mutex);
+    spinlock_lock(&dim->slock);
     bool is_empty = dim->km_contexts.empty();
-    netdata_mutex_unlock(&dim->mutex);
+    spinlock_unlock(&dim->slock);
 
     if (!is_empty)
         return 0;
@@ -602,7 +602,7 @@ int ml_dimension_load_models(RRDDIM *rd) {
     if (unlikely(rc != SQLITE_OK))
         goto bind_fail;
 
-    netdata_mutex_lock(&dim->mutex);
+    spinlock_lock(&dim->slock);
 
     dim->km_contexts.reserve(Cfg.num_models_to_use);
     while ((rc = sqlite3_step_monitored(res)) == SQLITE_ROW) {
@@ -639,7 +639,7 @@ int ml_dimension_load_models(RRDDIM *rd) {
         dim->ts = TRAINING_STATUS_TRAINED;
     }
 
-    netdata_mutex_unlock(&dim->mutex);
+    spinlock_unlock(&dim->slock);
 
     if (unlikely(rc != SQLITE_DONE))
         error_report("Failed to load models, rc = %d", rc);
@@ -666,7 +666,7 @@ ml_dimension_train_model(ml_training_thread_t *training_thread, ml_dimension_t *
     ml_training_response_t training_response = P.second;
 
     if (training_response.result != TRAINING_RESULT_OK) {
-        netdata_mutex_lock(&dim->mutex);
+        spinlock_lock(&dim->slock);
 
         dim->mt = METRIC_TYPE_CONSTANT;
 
@@ -687,7 +687,8 @@ ml_dimension_train_model(ml_training_thread_t *training_thread, ml_dimension_t *
 
         dim->last_training_time = training_response.last_entry_on_response;
         enum ml_training_result result = training_response.result;
-        netdata_mutex_unlock(&dim->mutex);
+
+        spinlock_unlock(&dim->slock);
 
         return result;
     }
@@ -713,7 +714,7 @@ ml_dimension_train_model(ml_training_thread_t *training_thread, ml_dimension_t *
     // update models
     worker_is_busy(WORKER_TRAIN_UPDATE_MODELS);
     {
-        netdata_mutex_lock(&dim->mutex);
+        spinlock_lock(&dim->slock);
 
         if (dim->km_contexts.size() < Cfg.num_models_to_use) {
             dim->km_contexts.push_back(std::move(dim->kmeans));
@@ -752,7 +753,7 @@ ml_dimension_train_model(ml_training_thread_t *training_thread, ml_dimension_t *
         model_info.kmeans = dim->km_contexts.back();
         training_thread->pending_model_info.push_back(model_info);
 
-        netdata_mutex_unlock(&dim->mutex);
+        spinlock_unlock(&dim->slock);
     }
 
     return training_response.result;
@@ -781,7 +782,7 @@ ml_dimension_schedule_for_training(ml_dimension_t *dim, time_t curr_time)
         break;
     case TRAINING_STATUS_SILENCED:
     case TRAINING_STATUS_TRAINED:
-        if ((dim->last_training_time + (Cfg.train_every * dim->rd->update_every)) < curr_time) {
+        if ((dim->last_training_time + (Cfg.train_every * dim->rd->rrdset->update_every)) < curr_time) {
             schedule_for_training = true;
             dim->ts = TRAINING_STATUS_PENDING_WITH_MODEL;
         }
@@ -851,7 +852,7 @@ ml_dimension_predict(ml_dimension_t *dim, time_t curr_time, calculated_number_t
     /*
      * Lock to predict and possibly schedule the dimension for training
     */
-    if (netdata_mutex_trylock(&dim->mutex) != 0)
+    if (spinlock_trylock(&dim->slock) == 0)
         return false;
 
     // Mark the metric time as variable if we received different values
@@ -866,7 +867,7 @@ ml_dimension_predict(ml_dimension_t *dim, time_t curr_time, calculated_number_t
         case TRAINING_STATUS_UNTRAINED:
         case TRAINING_STATUS_PENDING_WITHOUT_MODEL: {
         case TRAINING_STATUS_SILENCED:
-            netdata_mutex_unlock(&dim->mutex);
+            spinlock_unlock(&dim->slock);
             return false;
         }
         default:
@@ -891,7 +892,7 @@ ml_dimension_predict(ml_dimension_t *dim, time_t curr_time, calculated_number_t
 
         if (anomaly_score < (100 * Cfg.dimension_anomaly_score_threshold)) {
             global_statistics_ml_models_consulted(models_consulted);
-            netdata_mutex_unlock(&dim->mutex);
+            spinlock_unlock(&dim->slock);
             return false;
         }
 
@@ -905,7 +906,7 @@ ml_dimension_predict(ml_dimension_t *dim, time_t curr_time, calculated_number_t
         dim->ts = TRAINING_STATUS_SILENCED;
     }
 
-    netdata_mutex_unlock(&dim->mutex);
+    spinlock_unlock(&dim->slock);
 
     global_statistics_ml_models_consulted(models_consulted);
     return sum;
@@ -992,7 +993,7 @@ ml_host_detect_once(ml_host_t *host)
     host->mls = {};
     ml_machine_learning_stats_t mls_copy = {};
 
-    {
+    if (host->ml_running) {
         netdata_mutex_lock(&host->mutex);
 
         /*
@@ -1036,6 +1037,8 @@ ml_host_detect_once(ml_host_t *host)
         mls_copy = host->mls;
 
         netdata_mutex_unlock(&host->mutex);
+    } else {
+        host->host_anomaly_rate = 0.0;
     }
 
     worker_is_busy(WORKER_JOB_DETECTION_DIM_CHART);
@@ -1213,15 +1216,14 @@ void ml_host_new(RRDHOST *rh)
 
     host->rh = rh;
     host->mls = ml_machine_learning_stats_t();
-    //host->ts = ml_training_stats_t();
+    host->host_anomaly_rate = 0.0;
 
     static std::atomic<size_t> times_called(0);
     host->training_queue = Cfg.training_threads[times_called++ % Cfg.num_training_threads].training_queue;
 
-    host->host_anomaly_rate = 0.0;
-
     netdata_mutex_init(&host->mutex);
 
+    host->ml_running = true;
     rh->ml_host = (rrd_ml_host_t *) host;
 }
 
@@ -1237,6 +1239,70 @@ void ml_host_delete(RRDHOST *rh)
     rh->ml_host = NULL;
 }
 
+void ml_host_start(RRDHOST *rh) {
+    ml_host_t *host = (ml_host_t *) rh->ml_host;
+    if (!host)
+        return;
+
+    host->ml_running = true;
+}
+
+void ml_host_stop(RRDHOST *rh) {
+    ml_host_t *host = (ml_host_t *) rh->ml_host;
+    if (!host || !host->ml_running)
+        return;
+
+    netdata_mutex_lock(&host->mutex);
+
+    // reset host stats
+    host->mls = ml_machine_learning_stats_t();
+
+    // reset charts/dims
+    void *rsp = NULL;
+    rrdset_foreach_read(rsp, host->rh) {
+        RRDSET *rs = static_cast<RRDSET *>(rsp);
+
+        ml_chart_t *chart = (ml_chart_t *) rs->ml_chart;
+        if (!chart)
+            continue;
+
+        // reset chart
+        chart->mls = ml_machine_learning_stats_t();
+
+        void *rdp = NULL;
+        rrddim_foreach_read(rdp, rs) {
+            RRDDIM *rd = static_cast<RRDDIM *>(rdp);
+
+            ml_dimension_t *dim = (ml_dimension_t *) rd->ml_dimension;
+            if (!dim)
+                continue;
+
+            spinlock_lock(&dim->slock);
+
+            // reset dim
+            // TODO: should we drop in-mem models, or mark them as stale? Is it
+            // okay to resume training straight away?
+
+            dim->mt = METRIC_TYPE_CONSTANT;
+            dim->ts = TRAINING_STATUS_UNTRAINED;
+            dim->last_training_time = 0;
+            dim->suppression_anomaly_counter = 0;
+            dim->suppression_window_counter = 0;
+            dim->cns.clear();
+
+            ml_kmeans_init(&dim->kmeans);
+
+            spinlock_unlock(&dim->slock);
+        }
+        rrddim_foreach_done(rdp);
+    }
+    rrdset_foreach_done(rsp);
+
+    netdata_mutex_unlock(&host->mutex);
+
+    host->ml_running = false;
+}
+
 void ml_host_get_info(RRDHOST *rh, BUFFER *wb)
 {
     ml_host_t *host = (ml_host_t *) rh->ml_host;
@@ -1279,7 +1345,8 @@ void ml_host_get_detection_info(RRDHOST *rh, BUFFER *wb)
 
     netdata_mutex_lock(&host->mutex);
 
-    buffer_json_member_add_uint64(wb, "version", 1);
+    buffer_json_member_add_uint64(wb, "version", 2);
+    buffer_json_member_add_uint64(wb, "ml-running", host->ml_running);
     buffer_json_member_add_uint64(wb, "anomalous-dimensions", host->mls.num_anomalous_dimensions);
     buffer_json_member_add_uint64(wb, "normal-dimensions", host->mls.num_normal_dimensions);
     buffer_json_member_add_uint64(wb, "total-dimensions", host->mls.num_anomalous_dimensions +
@@ -1289,13 +1356,41 @@ void ml_host_get_detection_info(RRDHOST *rh, BUFFER *wb)
     netdata_mutex_unlock(&host->mutex);
 }
 
+bool ml_host_get_host_status(RRDHOST *rh, struct ml_metrics_statistics *mlm) {
+    ml_host_t *host = (ml_host_t *) rh->ml_host;
+    if (!host) {
+        memset(mlm, 0, sizeof(*mlm));
+        return false;
+    }
+
+    netdata_mutex_lock(&host->mutex);
+
+    mlm->anomalous = host->mls.num_anomalous_dimensions;
+    mlm->normal = host->mls.num_normal_dimensions;
+    mlm->trained = host->mls.num_training_status_trained + host->mls.num_training_status_pending_with_model;
+    mlm->pending = host->mls.num_training_status_untrained + host->mls.num_training_status_pending_without_model;
+    mlm->silenced = host->mls.num_training_status_silenced;
+
+    netdata_mutex_unlock(&host->mutex);
+
+    return true;
+}
+
+bool ml_host_running(RRDHOST *rh) {
+    ml_host_t *host = (ml_host_t *) rh->ml_host;
+    if(!host)
+        return false;
+
+    return true;
+}
+
 void ml_host_get_models(RRDHOST *rh, BUFFER *wb)
 {
     UNUSED(rh);
     UNUSED(wb);
 
     // TODO: To be implemented
-    error("Fetching KMeans models is not supported yet");
+    netdata_log_error("Fetching KMeans models is not supported yet");
 }
 
 void ml_chart_new(RRDSET *rs)
@@ -1309,8 +1404,6 @@ void ml_chart_new(RRDSET *rs)
     chart->rs = rs;
     chart->mls = ml_machine_learning_stats_t();
 
-    netdata_mutex_init(&chart->mutex);
-
     rs->ml_chart = (rrd_ml_chart_t *) chart;
 }
 
@@ -1322,8 +1415,6 @@ void ml_chart_delete(RRDSET *rs)
 
     ml_chart_t *chart = (ml_chart_t *) rs->ml_chart;
 
-    netdata_mutex_destroy(&chart->mutex);
-
     delete chart;
     rs->ml_chart = NULL;
 }
@@ -1334,7 +1425,6 @@ bool ml_chart_update_begin(RRDSET *rs)
     if (!chart)
         return false;
 
-    netdata_mutex_lock(&chart->mutex);
     chart->mls = {};
     return true;
 }
@@ -1344,8 +1434,6 @@ void ml_chart_update_end(RRDSET *rs)
     ml_chart_t *chart = (ml_chart_t *) rs->ml_chart;
     if (!chart)
         return;
-
-    netdata_mutex_unlock(&chart->mutex);
 }
 
 void ml_dimension_new(RRDDIM *rd)
@@ -1360,8 +1448,9 @@ void ml_dimension_new(RRDDIM *rd)
 
     dim->mt = METRIC_TYPE_CONSTANT;
     dim->ts = TRAINING_STATUS_UNTRAINED;
-
     dim->last_training_time = 0;
+    dim->suppression_anomaly_counter = 0;
+    dim->suppression_window_counter = 0;
 
     ml_kmeans_init(&dim->kmeans);
 
@@ -1370,7 +1459,7 @@ void ml_dimension_new(RRDDIM *rd)
     else
         dim->mls = MACHINE_LEARNING_STATUS_ENABLED;
 
-    netdata_mutex_init(&dim->mutex);
+    spinlock_init(&dim->slock);
 
     dim->km_contexts.reserve(Cfg.num_models_to_use);
 
@@ -1385,8 +1474,6 @@ void ml_dimension_delete(RRDDIM *rd)
     if (!dim)
         return;
 
-    netdata_mutex_destroy(&dim->mutex);
-
     delete dim;
     rd->ml_dimension = NULL;
 }
@@ -1397,6 +1484,10 @@ bool ml_dimension_is_anomalous(RRDDIM *rd, time_t curr_time, double value, bool
     if (!dim)
         return false;
 
+    ml_host_t *host = (ml_host_t *) rd->rrdset->rrdhost->ml_host;
+    if (!host->ml_running)
+        return false;
+
     ml_chart_t *chart = (ml_chart_t *) rd->rrdset->ml_chart;
 
     bool is_anomalous = ml_dimension_predict(dim, curr_time, value, exists);
@@ -1428,11 +1519,11 @@ static void ml_flush_pending_models(ml_training_thread_t *training_thread) {
 
     // try to rollback transaction if we got any failures
     if (rc) {
-        error("Trying to rollback ML transaction because it failed with rc=%d, op_no=%d", rc, op_no);
+        netdata_log_error("Trying to rollback ML transaction because it failed with rc=%d, op_no=%d", rc, op_no);
         op_no++;
         rc = db_execute(db, "ROLLBACK;");
         if (rc)
-            error("ML transaction rollback failed with rc=%d", rc);
+            netdata_log_error("ML transaction rollback failed with rc=%d", rc);
     }
 
     training_thread->pending_model_info.clear();
diff --git a/ml/ml.h b/ml/ml.h
index 964dd0821..9384f8a9e 100644
--- a/ml/ml.h
+++ b/ml/ml.h
@@ -23,6 +23,9 @@ void ml_stop_threads(void);
 void ml_host_new(RRDHOST *rh);
 void ml_host_delete(RRDHOST *rh);
 
+void ml_host_start(RRDHOST *RH);
+void ml_host_stop(RRDHOST *RH);
+
 void ml_host_get_info(RRDHOST *RH, BUFFER *wb);
 void ml_host_get_detection_info(RRDHOST *RH, BUFFER *wb);
 void ml_host_get_models(RRDHOST *RH, BUFFER *wb);
@@ -40,6 +43,9 @@ int ml_dimension_load_models(RRDDIM *rd);
 
 void ml_update_global_statistics_charts(uint64_t models_consulted);
 
+bool ml_host_get_host_status(RRDHOST *rh, struct ml_metrics_statistics *mlm);
+bool ml_host_running(RRDHOST *rh);
+
 #ifdef __cplusplus
 };
 #endif