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// SPDX-License-Identifier: GPL-3.0-or-later
#include "Config.h"
#include "Dimension.h"
#include "Chart.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, rrdhost_hostname(RH)))
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_host_new(RRDHOST *RH) {
if (!ml_enabled(RH))
return;
Host *H = new Host(RH);
RH->ml_host = reinterpret_cast<ml_host_t *>(H);
}
void ml_host_delete(RRDHOST *RH) {
Host *H = reinterpret_cast<Host *>(RH->ml_host);
if (!H)
return;
delete H;
RH->ml_host = nullptr;
}
void ml_chart_new(RRDSET *RS) {
Host *H = reinterpret_cast<Host *>(RS->rrdhost->ml_host);
if (!H)
return;
Chart *C = new Chart(RS);
RS->ml_chart = reinterpret_cast<ml_chart_t *>(C);
H->addChart(C);
}
void ml_chart_delete(RRDSET *RS) {
Host *H = reinterpret_cast<Host *>(RS->rrdhost->ml_host);
if (!H)
return;
Chart *C = reinterpret_cast<Chart *>(RS->ml_chart);
H->removeChart(C);
delete C;
RS->ml_chart = nullptr;
}
void ml_dimension_new(RRDDIM *RD) {
Chart *C = reinterpret_cast<Chart *>(RD->rrdset->ml_chart);
if (!C)
return;
Dimension *D = new Dimension(RD);
RD->ml_dimension = reinterpret_cast<ml_dimension_t *>(D);
C->addDimension(D);
}
void ml_dimension_delete(RRDDIM *RD) {
Dimension *D = reinterpret_cast<Dimension *>(RD->ml_dimension);
if (!D)
return;
Chart *C = reinterpret_cast<Chart *>(RD->rrdset->ml_chart);
C->removeDimension(D);
delete D;
RD->ml_dimension = nullptr;
}
char *ml_get_host_info(RRDHOST *RH) {
nlohmann::json ConfigJson;
if (RH && RH->ml_host) {
Host *H = reinterpret_cast<Host *>(RH->ml_host);
H->getConfigAsJson(ConfigJson);
} else {
ConfigJson["enabled"] = false;
}
return strdupz(ConfigJson.dump(2, '\t').c_str());
}
char *ml_get_host_runtime_info(RRDHOST *RH) {
nlohmann::json ConfigJson;
if (RH && RH->ml_host) {
Host *H = reinterpret_cast<Host *>(RH->ml_host);
H->getDetectionInfoAsJson(ConfigJson);
} else {
return nullptr;
}
return strdup(ConfigJson.dump(1, '\t').c_str());
}
char *ml_get_host_models(RRDHOST *RH) {
nlohmann::json ModelsJson;
if (RH && RH->ml_host) {
Host *H = reinterpret_cast<Host *>(RH->ml_host);
H->getModelsAsJson(ModelsJson);
return strdup(ModelsJson.dump(2, '\t').c_str());
}
return nullptr;
}
void ml_start_anomaly_detection_threads(RRDHOST *RH) {
if (RH && RH->ml_host) {
Host *H = reinterpret_cast<Host *>(RH->ml_host);
H->startAnomalyDetectionThreads();
}
}
void ml_stop_anomaly_detection_threads(RRDHOST *RH) {
if (RH && RH->ml_host) {
Host *H = reinterpret_cast<Host *>(RH->ml_host);
H->stopAnomalyDetectionThreads(true);
}
}
void ml_cancel_anomaly_detection_threads(RRDHOST *RH) {
if (RH && RH->ml_host) {
Host *H = reinterpret_cast<Host *>(RH->ml_host);
H->stopAnomalyDetectionThreads(false);
}
}
void ml_chart_update_begin(RRDSET *RS) {
Chart *C = reinterpret_cast<Chart *>(RS->ml_chart);
if (!C)
return;
C->updateBegin();
}
void ml_chart_update_end(RRDSET *RS) {
Chart *C = reinterpret_cast<Chart *>(RS->ml_chart);
if (!C)
return;
C->updateEnd();
}
bool ml_is_anomalous(RRDDIM *RD, time_t CurrT, double Value, bool Exists) {
Dimension *D = reinterpret_cast<Dimension *>(RD->ml_dimension);
if (!D)
return false;
Chart *C = reinterpret_cast<Chart *>(RD->rrdset->ml_chart);
bool IsAnomalous = D->predict(CurrT, Value, Exists);
C->updateDimension(D, IsAnomalous);
return IsAnomalous;
}
bool ml_streaming_enabled() {
return Cfg.StreamADCharts;
}
#include "ml-private.h"
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