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// SPDX-License-Identifier: GPL-3.0-or-later
#include "Config.h"
#include "Dimension.h"
#include "Host.h"
#include <random>
using namespace ml;
bool ml_capable() {
return true;
}
bool ml_enabled(RRDHOST *RH) {
if (!Cfg.EnableAnomalyDetection)
return false;
if (simple_pattern_matches(Cfg.SP_HostsToSkip, RH->hostname))
return false;
return true;
}
/*
* Assumptions:
* 1) hosts outlive their sets, and sets outlive their dimensions,
* 2) dimensions always have a set that has a host.
*/
void ml_init(void) {
// Read config values
Cfg.readMLConfig();
if (!Cfg.EnableAnomalyDetection)
return;
// Generate random numbers to efficiently sample the features we need
// for KMeans clustering.
std::random_device RD;
std::mt19937 Gen(RD());
Cfg.RandomNums.reserve(Cfg.MaxTrainSamples);
for (size_t Idx = 0; Idx != Cfg.MaxTrainSamples; Idx++)
Cfg.RandomNums.push_back(Gen());
}
void ml_new_host(RRDHOST *RH) {
if (!ml_enabled(RH))
return;
Host *H = new Host(RH);
RH->ml_host = static_cast<ml_host_t>(H);
H->startAnomalyDetectionThreads();
}
void ml_delete_host(RRDHOST *RH) {
Host *H = static_cast<Host *>(RH->ml_host);
if (!H)
return;
H->stopAnomalyDetectionThreads();
delete H;
RH->ml_host = nullptr;
}
void ml_new_dimension(RRDDIM *RD) {
RRDSET *RS = RD->rrdset;
Host *H = static_cast<Host *>(RD->rrdset->rrdhost->ml_host);
if (!H)
return;
if (static_cast<unsigned>(RD->update_every) != H->updateEvery())
return;
if (simple_pattern_matches(Cfg.SP_ChartsToSkip, RS->name))
return;
Dimension *D = new Dimension(RD);
RD->state->ml_dimension = static_cast<ml_dimension_t>(D);
H->addDimension(D);
}
void ml_delete_dimension(RRDDIM *RD) {
Dimension *D = static_cast<Dimension *>(RD->state->ml_dimension);
if (!D)
return;
Host *H = static_cast<Host *>(RD->rrdset->rrdhost->ml_host);
if (!H)
delete D;
else
H->removeDimension(D);
RD->state->ml_dimension = nullptr;
}
char *ml_get_host_info(RRDHOST *RH) {
nlohmann::json ConfigJson;
if (RH && RH->ml_host) {
Host *H = static_cast<Host *>(RH->ml_host);
H->getConfigAsJson(ConfigJson);
} else {
ConfigJson["enabled"] = false;
}
return strdup(ConfigJson.dump(2, '\t').c_str());
}
char *ml_get_host_runtime_info(RRDHOST *RH) {
nlohmann::json ConfigJson;
if (RH && RH->ml_host) {
Host *H = static_cast<Host *>(RH->ml_host);
H->getDetectionInfoAsJson(ConfigJson);
} else {
return nullptr;
}
return strdup(ConfigJson.dump(1, '\t').c_str());
}
bool ml_is_anomalous(RRDDIM *RD, double Value, bool Exists) {
Dimension *D = static_cast<Dimension *>(RD->state->ml_dimension);
if (!D)
return false;
D->addValue(Value, Exists);
bool Result = D->predict().second;
return Result;
}
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;
}
nlohmann::json Json = TimeRanges;
return strdup(Json.dump(4).c_str());
}
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;
}
return strdup(Json.dump(4, '\t').c_str());
}
void ml_process_rrdr(RRDR *R, int MaxAnomalyRates) {
if (R->rows != 1)
return;
if (MaxAnomalyRates < 1 || MaxAnomalyRates >= R->d)
return;
calculated_number *CNs = R->v;
RRDR_DIMENSION_FLAGS *DimFlags = R->od;
std::vector<std::pair<calculated_number, int>> V;
V.reserve(R->d);
for (int Idx = 0; Idx != R->d; Idx++)
V.emplace_back(CNs[Idx], Idx);
std::sort(V.rbegin(), V.rend());
for (int Idx = MaxAnomalyRates; Idx != R->d; Idx++) {
int UnsortedIdx = V[Idx].second;
int OldFlags = static_cast<int>(DimFlags[UnsortedIdx]);
int NewFlags = OldFlags | RRDR_DIMENSION_HIDDEN;
DimFlags[UnsortedIdx] = static_cast<rrdr_dimension_flag>(NewFlags);
}
}
void ml_dimension_update_name(RRDSET *RS, RRDDIM *RD, const char *Name) {
(void) RS;
Dimension *D = static_cast<Dimension *>(RD->state->ml_dimension);
if (!D)
return;
D->setAnomalyRateRDName(Name);
}
bool ml_streaming_enabled() {
return Cfg.StreamADCharts;
}
#if defined(ENABLE_ML_TESTS)
#include "gtest/gtest.h"
int test_ml(int argc, char *argv[]) {
(void) argc;
(void) argv;
::testing::InitGoogleTest(&argc, argv);
return RUN_ALL_TESTS();
}
#endif // ENABLE_ML_TESTS
#include "ml-private.h"
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