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

1 files changed, 36 insertions, 138 deletions

diff --git a/ml/Dimension.h b/ml/Dimension.h
index 8666710b6a..3ec56e0981 100644
--- a/ml/Dimension.h
+++ b/ml/Dimension.h
@@ -3,7 +3,6 @@
 #ifndef ML_DIMENSION_H
 #define ML_DIMENSION_H
 
-#include "Stats.h"
 #include "Query.h"
 #include "Config.h"
 
@@ -11,6 +10,12 @@
 
 namespace ml {
 
+enum class MLResult {
+    Success = 0,
+    MissingData,
+    NaN,
+};
+
 static inline std::string getMLDimensionID(RRDDIM *RD) {
     RRDSET *RS = RD->rrdset;
 
@@ -19,118 +24,16 @@ static inline std::string getMLDimensionID(RRDDIM *RD) {
     return SS.str();
 }
 
-enum class MachineLearningStatus {
-    // Enable training/prediction
-    Enabled,
-
-    // Disable due to update every being different from the host's
-    DisabledDueToUniqueUpdateEvery,
-
-    // Disable because configuration pattern matches the chart's id
-    DisabledDueToExcludedChart,
-};
-
-enum class TrainingStatus {
-    // We don't have a model for this dimension
-    Untrained,
-
-    // Request for training sent, but we don't have any models yet
-    PendingWithoutModel,
-
-    // Request to update existing models sent
-    PendingWithModel,
-
-    // Have a valid, up-to-date model
-    Trained,
-};
-
-enum class MetricType {
-    // The dimension has constant values, no need to train
-    Constant,
-
-    // The dimension's values fluctuate, we need to generate a model
-    Variable,
-};
-
-struct TrainingRequest {
-    // Chart/dimension we want to train
-    STRING *ChartId;
-    STRING *DimensionId;
-    
-    // Creation time of request
-    time_t RequestTime;
-    
-    // First/last entry of this dimension in DB
-    // at the point the request was made
-    time_t FirstEntryOnRequest;
-    time_t LastEntryOnRequest;
-};
-
-void dumpTrainingRequest(const TrainingRequest &TrainingReq, const char *Prefix);
-
-enum TrainingResult {
-    // We managed to create a KMeans model
-    Ok,
-    // Could not query DB with a correct time range
-    InvalidQueryTimeRange,
-    // Did not gather enough data from DB to run KMeans
-    NotEnoughCollectedValues,
-    // Acquired a null dimension
-    NullAcquiredDimension,
-    // Chart is under replication
-    ChartUnderReplication,
-};
-
-struct TrainingResponse {
-    // Time when the request for this response was made
-    time_t RequestTime;
-
-    // First/last entry of the dimension in DB when generating the request
-    time_t FirstEntryOnRequest;
-    time_t LastEntryOnRequest;
-    
-    // First/last entry of the dimension in DB when generating the response
-    time_t FirstEntryOnResponse;
-    time_t LastEntryOnResponse;
-    
-    // After/Before timestamps of our DB query
-    time_t QueryAfterT;
-    time_t QueryBeforeT;
-    
-    // Actual after/before returned by the DB query ops
-    time_t DbAfterT;
-    time_t DbBeforeT;
-    
-    // Number of doubles returned by the DB query
-    size_t CollectedValues;
-    
-    // Number of values we return to the caller
-    size_t TotalValues;
-
-    // Result of training response
-    TrainingResult Result;
-};
-
-void dumpTrainingResponse(const TrainingResponse &TrainingResp, const char *Prefix);
-
 class Dimension {
 public:
     Dimension(RRDDIM *RD) :
         RD(RD),
-        MT(MetricType::Constant),
-        TS(TrainingStatus::Untrained),
-        TR(),
-        LastTrainingTime(0)
-    {
-        if (simple_pattern_matches(Cfg.SP_ChartsToSkip, rrdset_name(RD->rrdset)))
-            MLS = MachineLearningStatus::DisabledDueToExcludedChart;
-        else if (RD->update_every != RD->rrdset->rrdhost->rrd_update_every)
-            MLS = MachineLearningStatus::DisabledDueToUniqueUpdateEvery;
-        else
-            MLS = MachineLearningStatus::Enabled;
-
-        Models.reserve(Cfg.NumModelsToUse);
-    }
+        LastTrainedAt(Seconds(0)),
+        Trained(false),
+        ConstantModel(false),
+        AnomalyScore(0.0),
+        AnomalyBit(0)
+    { }
 
     RRDDIM *getRD() const {
         return RD;
@@ -140,54 +43,49 @@ public:
         return RD->update_every;
     }
 
-    MetricType getMT() const {
-        return MT;
+    time_t latestTime() const {
+        return Query(RD).latestTime();
     }
 
-    TrainingStatus getTS() const {
-        return TS;
+    time_t oldestTime() const {
+        return Query(RD).oldestTime();
     }
 
-    MachineLearningStatus getMLS() const {
-        return MLS;
+    bool isTrained() const {
+        return Trained;
     }
 
-    TrainingResult trainModel(const TrainingRequest &TR);
+    bool isAnomalous() const {
+        return AnomalyBit;
+    }
 
-    void scheduleForTraining(time_t CurrT);
+    bool shouldTrain(const TimePoint &TP) const;
 
-    bool predict(time_t CurrT, CalculatedNumber Value, bool Exists);
+    bool isActive() const;
 
-    std::vector<KMeans> getModels();
-    
-    void dump() const;
+    MLResult trainModel();
 
-private:
-    TrainingRequest getTrainingRequest(time_t CurrT) const {
-        return TrainingRequest {
-            string_dup(RD->rrdset->id),
-            string_dup(RD->id),
-            CurrT,
-            rrddim_first_entry_t(RD),
-            rrddim_last_entry_t(RD)
-        };
-    }
+    bool predict(CalculatedNumber Value, bool Exists);
+
+    std::pair<bool, double> detect(size_t WindowLength, bool Reset);
+
+    std::array<KMeans, 1> getModels();
 
 private:
-    std::pair<CalculatedNumber *, TrainingResponse> getCalculatedNumbers(const TrainingRequest &TrainingReq);
+    std::pair<CalculatedNumber *, size_t> getCalculatedNumbers();
 
 public:
     RRDDIM *RD;
-    MetricType MT;
-    TrainingStatus TS;
-    TrainingResponse TR;
 
-    time_t LastTrainingTime;
+    TimePoint LastTrainedAt;
+    std::atomic<bool> Trained;
+    std::atomic<bool> ConstantModel;
 
-    MachineLearningStatus MLS;
+    CalculatedNumber AnomalyScore;
+    std::atomic<bool> AnomalyBit;
 
     std::vector<CalculatedNumber> CNs;
-    std::vector<KMeans> Models;
+    std::array<KMeans, 1> Models;
     std::mutex Mutex;
 };