Adding upstream version 1.39.1.upstream/1.39.1

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

1 files changed, 67 insertions, 18 deletions

diff --git a/ml/ml.cc b/ml/ml.cc
index 1f49f4bf..34f2b93b 100644
--- a/ml/ml.cc
+++ b/ml/ml.cc
@@ -63,6 +63,8 @@ ml_training_status_to_string(enum ml_training_status ts)
             return "trained";
         case TRAINING_STATUS_UNTRAINED:
             return "untrained";
+        case TRAINING_STATUS_SILENCED:
+            return "silenced";
         default:
             return "unknown";
     }
@@ -490,12 +492,16 @@ ml_dimension_add_model(const uuid_t *metric_uuid, const ml_kmeans_t *km)
     }
 
     rc = execute_insert(res);
-    if (unlikely(rc != SQLITE_DONE))
+    if (unlikely(rc != SQLITE_DONE)) {
         error_report("Failed to store model, rc = %d", rc);
+        return rc;
+    }
 
     rc = sqlite3_reset(res);
-    if (unlikely(rc != SQLITE_OK))
+    if (unlikely(rc != SQLITE_OK)) {
         error_report("Failed to reset statement when storing model, rc = %d", rc);
+        return rc;
+    }
 
     return 0;
 
@@ -504,7 +510,7 @@ bind_fail:
     rc = sqlite3_reset(res);
     if (unlikely(rc != SQLITE_OK))
         error_report("Failed to reset statement to store model, rc = %d", rc);
-    return 1;
+    return rc;
 }
 
 static int
@@ -523,7 +529,7 @@ ml_dimension_delete_models(const uuid_t *metric_uuid, time_t before)
         rc = prepare_statement(db, db_models_delete, &res);
         if (unlikely(rc != SQLITE_OK)) {
             error_report("Failed to prepare statement to delete models, rc = %d", rc);
-            return 1;
+            return rc;
         }
     }
 
@@ -536,12 +542,16 @@ ml_dimension_delete_models(const uuid_t *metric_uuid, time_t before)
         goto bind_fail;
 
     rc = execute_insert(res);
-    if (unlikely(rc != SQLITE_DONE))
+    if (unlikely(rc != SQLITE_DONE)) {
         error_report("Failed to delete models, rc = %d", rc);
+        return rc;
+    }
 
     rc = sqlite3_reset(res);
-    if (unlikely(rc != SQLITE_OK))
+    if (unlikely(rc != SQLITE_OK)) {
         error_report("Failed to reset statement when deleting models, rc = %d", rc);
+        return rc;
+    }
 
     return 0;
 
@@ -550,7 +560,7 @@ bind_fail:
     rc = sqlite3_reset(res);
     if (unlikely(rc != SQLITE_OK))
         error_report("Failed to reset statement to delete models, rc = %d", rc);
-    return 1;
+    return rc;
 }
 
 int ml_dimension_load_models(RRDDIM *rd) {
@@ -671,6 +681,8 @@ ml_dimension_train_model(ml_training_thread_t *training_thread, ml_dimension_t *
                 break;
         }
 
+        dim->suppression_anomaly_counter = 0;
+        dim->suppression_window_counter = 0;
         dim->tr = training_response;
 
         dim->last_training_time = training_response.last_entry_on_response;
@@ -727,6 +739,10 @@ ml_dimension_train_model(ml_training_thread_t *training_thread, ml_dimension_t *
 
         dim->mt = METRIC_TYPE_CONSTANT;
         dim->ts = TRAINING_STATUS_TRAINED;
+
+        dim->suppression_anomaly_counter = 0;
+        dim->suppression_window_counter = 0;
+
         dim->tr = training_response;
         dim->last_training_time = rrddim_last_entry_s(dim->rd);
 
@@ -763,6 +779,7 @@ ml_dimension_schedule_for_training(ml_dimension_t *dim, time_t curr_time)
         schedule_for_training = true;
         dim->ts = TRAINING_STATUS_PENDING_WITHOUT_MODEL;
         break;
+    case TRAINING_STATUS_SILENCED:
     case TRAINING_STATUS_TRAINED:
         if ((dim->last_training_time + (Cfg.train_every * dim->rd->update_every)) < curr_time) {
             schedule_for_training = true;
@@ -848,6 +865,7 @@ ml_dimension_predict(ml_dimension_t *dim, time_t curr_time, calculated_number_t
     switch (dim->ts) {
         case TRAINING_STATUS_UNTRAINED:
         case TRAINING_STATUS_PENDING_WITHOUT_MODEL: {
+        case TRAINING_STATUS_SILENCED:
             netdata_mutex_unlock(&dim->mutex);
             return false;
         }
@@ -855,6 +873,8 @@ ml_dimension_predict(ml_dimension_t *dim, time_t curr_time, calculated_number_t
             break;
     }
 
+    dim->suppression_window_counter++;
+
     /*
      * Use the KMeans models to check if the value is anomalous
     */
@@ -878,6 +898,13 @@ ml_dimension_predict(ml_dimension_t *dim, time_t curr_time, calculated_number_t
         sum += 1;
     }
 
+    dim->suppression_anomaly_counter += sum ? 1 : 0;
+
+    if ((dim->suppression_anomaly_counter >= Cfg.suppression_threshold) &&
+        (dim->suppression_window_counter >= Cfg.suppression_window)) {
+        dim->ts = TRAINING_STATUS_SILENCED;
+    }
+
     netdata_mutex_unlock(&dim->mutex);
 
     global_statistics_ml_models_consulted(models_consulted);
@@ -934,6 +961,13 @@ ml_chart_update_dimension(ml_chart_t *chart, ml_dimension_t *dim, bool is_anomal
                     chart->mls.num_anomalous_dimensions += is_anomalous;
                     chart->mls.num_normal_dimensions += !is_anomalous;
                     return;
+                case TRAINING_STATUS_SILENCED:
+                    chart->mls.num_training_status_silenced++;
+                    chart->mls.num_training_status_trained++;
+
+                    chart->mls.num_anomalous_dimensions += is_anomalous;
+                    chart->mls.num_normal_dimensions += !is_anomalous;
+                    return;
             }
 
             return;
@@ -987,6 +1021,7 @@ ml_host_detect_once(ml_host_t *host)
             host->mls.num_training_status_pending_without_model += chart_mls.num_training_status_pending_without_model;
             host->mls.num_training_status_trained += chart_mls.num_training_status_trained;
             host->mls.num_training_status_pending_with_model += chart_mls.num_training_status_pending_with_model;
+            host->mls.num_training_status_silenced += chart_mls.num_training_status_silenced;
 
             host->mls.num_anomalous_dimensions += chart_mls.num_anomalous_dimensions;
             host->mls.num_normal_dimensions += chart_mls.num_normal_dimensions;
@@ -1370,23 +1405,37 @@ bool ml_dimension_is_anomalous(RRDDIM *rd, time_t curr_time, double value, bool
     return is_anomalous;
 }
 
-static int ml_flush_pending_models(ml_training_thread_t *training_thread) {
-    (void) db_execute(db, "BEGIN TRANSACTION;");
+static void ml_flush_pending_models(ml_training_thread_t *training_thread) {
+    int rc = db_execute(db, "BEGIN TRANSACTION;");
+    int op_no = 1;
 
-    for (const auto &pending_model: training_thread->pending_model_info) {
-        int rc = ml_dimension_add_model(&pending_model.metric_uuid, &pending_model.kmeans);
-        if (rc)
-            return rc;
+    if (!rc) {
+        op_no++;
 
-        rc = ml_dimension_delete_models(&pending_model.metric_uuid, pending_model.kmeans.before - (Cfg.num_models_to_use * Cfg.train_every));
-        if (rc)
-            return rc;
+        for (const auto &pending_model: training_thread->pending_model_info) {
+            if (!rc)
+                rc = ml_dimension_add_model(&pending_model.metric_uuid, &pending_model.kmeans);
+
+            if (!rc)
+                rc = ml_dimension_delete_models(&pending_model.metric_uuid, pending_model.kmeans.before - (Cfg.num_models_to_use * Cfg.train_every));
+        }
+    }
+
+    if (!rc) {
+        op_no++;
+        rc = db_execute(db, "COMMIT TRANSACTION;");
     }
 
-    (void) db_execute(db, "COMMIT TRANSACTION;");
+    // 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);
+        op_no++;
+        rc = db_execute(db, "ROLLBACK;");
+        if (rc)
+            error("ML transaction rollback failed with rc=%d", rc);
+    }
 
     training_thread->pending_model_info.clear();
-    return 0;
 }
 
 static void *ml_train_main(void *arg) {