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-rw-r--r--ml/ml.cc157
1 files changed, 124 insertions, 33 deletions
diff --git a/ml/ml.cc b/ml/ml.cc
index a5f0fa062..396967492 100644
--- a/ml/ml.cc
+++ b/ml/ml.cc
@@ -337,7 +337,7 @@ ml_dimension_calculated_numbers(ml_training_thread_t *training_thread, ml_dimens
// Figure out what our time window should be.
training_response.query_before_t = training_response.last_entry_on_response;
training_response.query_after_t = std::max(
- training_response.query_before_t - static_cast<time_t>((max_n - 1) * dim->rd->update_every),
+ training_response.query_before_t - static_cast<time_t>((max_n - 1) * dim->rd->rrdset->update_every),
training_response.first_entry_on_response
);
@@ -568,9 +568,9 @@ int ml_dimension_load_models(RRDDIM *rd) {
if (!dim)
return 0;
- netdata_mutex_lock(&dim->mutex);
+ spinlock_lock(&dim->slock);
bool is_empty = dim->km_contexts.empty();
- netdata_mutex_unlock(&dim->mutex);
+ spinlock_unlock(&dim->slock);
if (!is_empty)
return 0;
@@ -602,7 +602,7 @@ int ml_dimension_load_models(RRDDIM *rd) {
if (unlikely(rc != SQLITE_OK))
goto bind_fail;
- netdata_mutex_lock(&dim->mutex);
+ spinlock_lock(&dim->slock);
dim->km_contexts.reserve(Cfg.num_models_to_use);
while ((rc = sqlite3_step_monitored(res)) == SQLITE_ROW) {
@@ -639,7 +639,7 @@ int ml_dimension_load_models(RRDDIM *rd) {
dim->ts = TRAINING_STATUS_TRAINED;
}
- netdata_mutex_unlock(&dim->mutex);
+ spinlock_unlock(&dim->slock);
if (unlikely(rc != SQLITE_DONE))
error_report("Failed to load models, rc = %d", rc);
@@ -666,7 +666,7 @@ ml_dimension_train_model(ml_training_thread_t *training_thread, ml_dimension_t *
ml_training_response_t training_response = P.second;
if (training_response.result != TRAINING_RESULT_OK) {
- netdata_mutex_lock(&dim->mutex);
+ spinlock_lock(&dim->slock);
dim->mt = METRIC_TYPE_CONSTANT;
@@ -687,7 +687,8 @@ ml_dimension_train_model(ml_training_thread_t *training_thread, ml_dimension_t *
dim->last_training_time = training_response.last_entry_on_response;
enum ml_training_result result = training_response.result;
- netdata_mutex_unlock(&dim->mutex);
+
+ spinlock_unlock(&dim->slock);
return result;
}
@@ -713,7 +714,7 @@ ml_dimension_train_model(ml_training_thread_t *training_thread, ml_dimension_t *
// update models
worker_is_busy(WORKER_TRAIN_UPDATE_MODELS);
{
- netdata_mutex_lock(&dim->mutex);
+ spinlock_lock(&dim->slock);
if (dim->km_contexts.size() < Cfg.num_models_to_use) {
dim->km_contexts.push_back(std::move(dim->kmeans));
@@ -752,7 +753,7 @@ ml_dimension_train_model(ml_training_thread_t *training_thread, ml_dimension_t *
model_info.kmeans = dim->km_contexts.back();
training_thread->pending_model_info.push_back(model_info);
- netdata_mutex_unlock(&dim->mutex);
+ spinlock_unlock(&dim->slock);
}
return training_response.result;
@@ -781,7 +782,7 @@ ml_dimension_schedule_for_training(ml_dimension_t *dim, time_t curr_time)
break;
case TRAINING_STATUS_SILENCED:
case TRAINING_STATUS_TRAINED:
- if ((dim->last_training_time + (Cfg.train_every * dim->rd->update_every)) < curr_time) {
+ if ((dim->last_training_time + (Cfg.train_every * dim->rd->rrdset->update_every)) < curr_time) {
schedule_for_training = true;
dim->ts = TRAINING_STATUS_PENDING_WITH_MODEL;
}
@@ -851,7 +852,7 @@ ml_dimension_predict(ml_dimension_t *dim, time_t curr_time, calculated_number_t
/*
* Lock to predict and possibly schedule the dimension for training
*/
- if (netdata_mutex_trylock(&dim->mutex) != 0)
+ if (spinlock_trylock(&dim->slock) == 0)
return false;
// Mark the metric time as variable if we received different values
@@ -866,7 +867,7 @@ ml_dimension_predict(ml_dimension_t *dim, time_t curr_time, calculated_number_t
case TRAINING_STATUS_UNTRAINED:
case TRAINING_STATUS_PENDING_WITHOUT_MODEL: {
case TRAINING_STATUS_SILENCED:
- netdata_mutex_unlock(&dim->mutex);
+ spinlock_unlock(&dim->slock);
return false;
}
default:
@@ -891,7 +892,7 @@ ml_dimension_predict(ml_dimension_t *dim, time_t curr_time, calculated_number_t
if (anomaly_score < (100 * Cfg.dimension_anomaly_score_threshold)) {
global_statistics_ml_models_consulted(models_consulted);
- netdata_mutex_unlock(&dim->mutex);
+ spinlock_unlock(&dim->slock);
return false;
}
@@ -905,7 +906,7 @@ ml_dimension_predict(ml_dimension_t *dim, time_t curr_time, calculated_number_t
dim->ts = TRAINING_STATUS_SILENCED;
}
- netdata_mutex_unlock(&dim->mutex);
+ spinlock_unlock(&dim->slock);
global_statistics_ml_models_consulted(models_consulted);
return sum;
@@ -992,7 +993,7 @@ ml_host_detect_once(ml_host_t *host)
host->mls = {};
ml_machine_learning_stats_t mls_copy = {};
- {
+ if (host->ml_running) {
netdata_mutex_lock(&host->mutex);
/*
@@ -1036,6 +1037,8 @@ ml_host_detect_once(ml_host_t *host)
mls_copy = host->mls;
netdata_mutex_unlock(&host->mutex);
+ } else {
+ host->host_anomaly_rate = 0.0;
}
worker_is_busy(WORKER_JOB_DETECTION_DIM_CHART);
@@ -1213,15 +1216,14 @@ void ml_host_new(RRDHOST *rh)
host->rh = rh;
host->mls = ml_machine_learning_stats_t();
- //host->ts = ml_training_stats_t();
+ host->host_anomaly_rate = 0.0;
static std::atomic<size_t> times_called(0);
host->training_queue = Cfg.training_threads[times_called++ % Cfg.num_training_threads].training_queue;
- host->host_anomaly_rate = 0.0;
-
netdata_mutex_init(&host->mutex);
+ host->ml_running = true;
rh->ml_host = (rrd_ml_host_t *) host;
}
@@ -1237,6 +1239,70 @@ void ml_host_delete(RRDHOST *rh)
rh->ml_host = NULL;
}
+void ml_host_start(RRDHOST *rh) {
+ ml_host_t *host = (ml_host_t *) rh->ml_host;
+ if (!host)
+ return;
+
+ host->ml_running = true;
+}
+
+void ml_host_stop(RRDHOST *rh) {
+ ml_host_t *host = (ml_host_t *) rh->ml_host;
+ if (!host || !host->ml_running)
+ return;
+
+ netdata_mutex_lock(&host->mutex);
+
+ // reset host stats
+ host->mls = ml_machine_learning_stats_t();
+
+ // reset charts/dims
+ void *rsp = NULL;
+ rrdset_foreach_read(rsp, host->rh) {
+ RRDSET *rs = static_cast<RRDSET *>(rsp);
+
+ ml_chart_t *chart = (ml_chart_t *) rs->ml_chart;
+ if (!chart)
+ continue;
+
+ // reset chart
+ chart->mls = ml_machine_learning_stats_t();
+
+ void *rdp = NULL;
+ rrddim_foreach_read(rdp, rs) {
+ RRDDIM *rd = static_cast<RRDDIM *>(rdp);
+
+ ml_dimension_t *dim = (ml_dimension_t *) rd->ml_dimension;
+ if (!dim)
+ continue;
+
+ spinlock_lock(&dim->slock);
+
+ // reset dim
+ // TODO: should we drop in-mem models, or mark them as stale? Is it
+ // okay to resume training straight away?
+
+ dim->mt = METRIC_TYPE_CONSTANT;
+ dim->ts = TRAINING_STATUS_UNTRAINED;
+ dim->last_training_time = 0;
+ dim->suppression_anomaly_counter = 0;
+ dim->suppression_window_counter = 0;
+ dim->cns.clear();
+
+ ml_kmeans_init(&dim->kmeans);
+
+ spinlock_unlock(&dim->slock);
+ }
+ rrddim_foreach_done(rdp);
+ }
+ rrdset_foreach_done(rsp);
+
+ netdata_mutex_unlock(&host->mutex);
+
+ host->ml_running = false;
+}
+
void ml_host_get_info(RRDHOST *rh, BUFFER *wb)
{
ml_host_t *host = (ml_host_t *) rh->ml_host;
@@ -1279,7 +1345,8 @@ void ml_host_get_detection_info(RRDHOST *rh, BUFFER *wb)
netdata_mutex_lock(&host->mutex);
- buffer_json_member_add_uint64(wb, "version", 1);
+ buffer_json_member_add_uint64(wb, "version", 2);
+ buffer_json_member_add_uint64(wb, "ml-running", host->ml_running);
buffer_json_member_add_uint64(wb, "anomalous-dimensions", host->mls.num_anomalous_dimensions);
buffer_json_member_add_uint64(wb, "normal-dimensions", host->mls.num_normal_dimensions);
buffer_json_member_add_uint64(wb, "total-dimensions", host->mls.num_anomalous_dimensions +
@@ -1289,13 +1356,41 @@ void ml_host_get_detection_info(RRDHOST *rh, BUFFER *wb)
netdata_mutex_unlock(&host->mutex);
}
+bool ml_host_get_host_status(RRDHOST *rh, struct ml_metrics_statistics *mlm) {
+ ml_host_t *host = (ml_host_t *) rh->ml_host;
+ if (!host) {
+ memset(mlm, 0, sizeof(*mlm));
+ return false;
+ }
+
+ netdata_mutex_lock(&host->mutex);
+
+ mlm->anomalous = host->mls.num_anomalous_dimensions;
+ mlm->normal = host->mls.num_normal_dimensions;
+ mlm->trained = host->mls.num_training_status_trained + host->mls.num_training_status_pending_with_model;
+ mlm->pending = host->mls.num_training_status_untrained + host->mls.num_training_status_pending_without_model;
+ mlm->silenced = host->mls.num_training_status_silenced;
+
+ netdata_mutex_unlock(&host->mutex);
+
+ return true;
+}
+
+bool ml_host_running(RRDHOST *rh) {
+ ml_host_t *host = (ml_host_t *) rh->ml_host;
+ if(!host)
+ return false;
+
+ return true;
+}
+
void ml_host_get_models(RRDHOST *rh, BUFFER *wb)
{
UNUSED(rh);
UNUSED(wb);
// TODO: To be implemented
- error("Fetching KMeans models is not supported yet");
+ netdata_log_error("Fetching KMeans models is not supported yet");
}
void ml_chart_new(RRDSET *rs)
@@ -1309,8 +1404,6 @@ void ml_chart_new(RRDSET *rs)
chart->rs = rs;
chart->mls = ml_machine_learning_stats_t();
- netdata_mutex_init(&chart->mutex);
-
rs->ml_chart = (rrd_ml_chart_t *) chart;
}
@@ -1322,8 +1415,6 @@ void ml_chart_delete(RRDSET *rs)
ml_chart_t *chart = (ml_chart_t *) rs->ml_chart;
- netdata_mutex_destroy(&chart->mutex);
-
delete chart;
rs->ml_chart = NULL;
}
@@ -1334,7 +1425,6 @@ bool ml_chart_update_begin(RRDSET *rs)
if (!chart)
return false;
- netdata_mutex_lock(&chart->mutex);
chart->mls = {};
return true;
}
@@ -1344,8 +1434,6 @@ void ml_chart_update_end(RRDSET *rs)
ml_chart_t *chart = (ml_chart_t *) rs->ml_chart;
if (!chart)
return;
-
- netdata_mutex_unlock(&chart->mutex);
}
void ml_dimension_new(RRDDIM *rd)
@@ -1360,8 +1448,9 @@ void ml_dimension_new(RRDDIM *rd)
dim->mt = METRIC_TYPE_CONSTANT;
dim->ts = TRAINING_STATUS_UNTRAINED;
-
dim->last_training_time = 0;
+ dim->suppression_anomaly_counter = 0;
+ dim->suppression_window_counter = 0;
ml_kmeans_init(&dim->kmeans);
@@ -1370,7 +1459,7 @@ void ml_dimension_new(RRDDIM *rd)
else
dim->mls = MACHINE_LEARNING_STATUS_ENABLED;
- netdata_mutex_init(&dim->mutex);
+ spinlock_init(&dim->slock);
dim->km_contexts.reserve(Cfg.num_models_to_use);
@@ -1385,8 +1474,6 @@ void ml_dimension_delete(RRDDIM *rd)
if (!dim)
return;
- netdata_mutex_destroy(&dim->mutex);
-
delete dim;
rd->ml_dimension = NULL;
}
@@ -1397,6 +1484,10 @@ bool ml_dimension_is_anomalous(RRDDIM *rd, time_t curr_time, double value, bool
if (!dim)
return false;
+ ml_host_t *host = (ml_host_t *) rd->rrdset->rrdhost->ml_host;
+ if (!host->ml_running)
+ return false;
+
ml_chart_t *chart = (ml_chart_t *) rd->rrdset->ml_chart;
bool is_anomalous = ml_dimension_predict(dim, curr_time, value, exists);
@@ -1428,11 +1519,11 @@ static void ml_flush_pending_models(ml_training_thread_t *training_thread) {
// 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);
+ netdata_log_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);
+ netdata_log_error("ML transaction rollback failed with rc=%d", rc);
}
training_thread->pending_model_info.clear();