Remove anomaly detector (#13657)

* Move all dims under one class.

* Dimension owns anomaly rate RD.

* Remove Dimension::isAnomalous()

* Remove Dimension::trainEvery()

* Rm ml/kmeans

* Remove anomaly detector

The same logic can be implemented by using the host anomaly rate dim.

* Profile plugin.

* Revert "Profile plugin."

This reverts commit e3db37cb49c514502c5216cfe7bca2a003fb90f1.

* Add separate source files for anomaly detection charts.

* Handle training/prediction sync at the dimension level.

* Keep multiple KMeans models in mem.

* Move feature extraction outside KMeans class.

* Use multiple models.

* Add /api/v1/ml_models endpoint.

* Remove Dimension::getID()

* Use just 1 model and fix tests.

* Add detection logic based on rrdr.

* Remove config options related to anomaly detection.

* Make anomaly detection queries configurable.

* Fix ad query duration option.

* Finalize queries in all code paths.

* Check if query was initialized before finalizing it

* Do not leak OWA

* Profile plugin.

* Revert "Profile plugin."

This reverts commit 5c77145d0df7e091d030476c480ab8d9cbceb89e.

* Change context from anomaly_detection to detector_events.

1 files changed, 13 insertions, 48 deletions

diff --git a/ml/ml.cc b/ml/ml.cc
index a7111312eb..60ea8d60cb 100644
--- a/ml/ml.cc
+++ b/ml/ml.cc
@@ -82,7 +82,7 @@ void ml_new_dimension(RRDDIM *RD) {
     if (rrdset_is_ar_chart(RS))
         return;
 
-    Dimension *D = new Dimension(RD);
+    Dimension *D = new Dimension(RD, H->getAnomalyRateRS());
     RD->ml_dimension = static_cast<ml_dimension_t>(D);
     H->addDimension(D);
 }
@@ -127,59 +127,24 @@ char *ml_get_host_runtime_info(RRDHOST *RH) {
     return strdup(ConfigJson.dump(1, '\t').c_str());
 }
 
-bool ml_is_anomalous(RRDDIM *RD, double Value, bool Exists) {
-    Dimension *D = static_cast<Dimension *>(RD->ml_dimension);
-    if (!D)
-        return false;
-
-    D->addValue(Value, Exists);
-    bool Result = D->predict().second;
-    return Result;
-}
+char *ml_get_host_models(RRDHOST *RH) {
+    nlohmann::json ModelsJson;
 
-char *ml_get_anomaly_events(RRDHOST *RH, const char *AnomalyDetectorName,
-                            int AnomalyDetectorVersion, time_t After, time_t Before) {
-    if (!RH || !RH->ml_host) {
-        error("No host");
-        return nullptr;
-    }
-
-    Host *H = static_cast<Host *>(RH->ml_host);
-    std::vector<std::pair<time_t, time_t>> TimeRanges;
-
-    bool Res = H->getAnomaliesInRange(TimeRanges, AnomalyDetectorName,
-                                                  AnomalyDetectorVersion,
-                                                  H->getUUID(),
-                                                  After, Before);
-    if (!Res) {
-        error("DB result is empty");
-        return nullptr;
+    if (RH && RH->ml_host) {
+        Host *H = static_cast<Host *>(RH->ml_host);
+        H->getModelsAsJson(ModelsJson);
+        return strdup(ModelsJson.dump(2, '\t').c_str());
     }
 
-    nlohmann::json Json = TimeRanges;
-    return strdup(Json.dump(4).c_str());
+    return nullptr;
 }
 
-char *ml_get_anomaly_event_info(RRDHOST *RH, const char *AnomalyDetectorName,
-                                int AnomalyDetectorVersion, time_t After, time_t Before) {
-    if (!RH || !RH->ml_host) {
-        error("No host");
-        return nullptr;
-    }
-
-    Host *H = static_cast<Host *>(RH->ml_host);
-
-    nlohmann::json Json;
-    bool Res = H->getAnomalyInfo(Json, AnomalyDetectorName,
-                                       AnomalyDetectorVersion,
-                                       H->getUUID(),
-                                       After, Before);
-    if (!Res) {
-        error("DB result is empty");
-        return nullptr;
-    }
+bool ml_is_anomalous(RRDDIM *RD, double Value, bool Exists) {
+    Dimension *D = static_cast<Dimension *>(RD->ml_dimension);
+    if (!D)
+        return false;
 
-    return strdup(Json.dump(4, '\t').c_str());
+    return D->predict(Value, Exists);
 }
 
 void ml_process_rrdr(RRDR *R, int MaxAnomalyRates) {