diff --git a/collectors/all.h b/collectors/all.h
index 653729bbcb..e99c097c12 100644
--- a/collectors/all.h
+++ b/collectors/all.h
@@ -395,16 +395,17 @@
#define ML_CHART_PRIO_DETECTOR_EVENTS 39183
// [netdata.ml] charts
-#define NETDATA_ML_CHART_PRIO_MACHINE_LEARNING_STATUS 890001
-#define NETDATA_ML_CHART_PRIO_METRIC_TYPES 890002
-#define NETDATA_ML_CHART_PRIO_TRAINING_STATUS 890003
+#define NETDATA_ML_CHART_RUNNING 890001
+#define NETDATA_ML_CHART_PRIO_MACHINE_LEARNING_STATUS 890002
+#define NETDATA_ML_CHART_PRIO_METRIC_TYPES 890003
+#define NETDATA_ML_CHART_PRIO_TRAINING_STATUS 890004
-#define NETDATA_ML_CHART_PRIO_PREDICTION_USAGE 890004
-#define NETDATA_ML_CHART_PRIO_TRAINING_USAGE 890005
+#define NETDATA_ML_CHART_PRIO_PREDICTION_USAGE 890005
+#define NETDATA_ML_CHART_PRIO_TRAINING_USAGE 890006
-#define NETDATA_ML_CHART_PRIO_QUEUE_STATS 890006
-#define NETDATA_ML_CHART_PRIO_TRAINING_TIME_STATS 890007
-#define NETDATA_ML_CHART_PRIO_TRAINING_RESULTS 890008
+#define NETDATA_ML_CHART_PRIO_QUEUE_STATS 890007
+#define NETDATA_ML_CHART_PRIO_TRAINING_TIME_STATS 890008
+#define NETDATA_ML_CHART_PRIO_TRAINING_RESULTS 890009
#define NETDATA_ML_CHART_FAMILY "machine learning"
#define NETDATA_ML_PLUGIN "ml.plugin"
diff --git a/daemon/main.c b/daemon/main.c
index cff6530f3f..15f6b1dd61 100644
--- a/daemon/main.c
+++ b/daemon/main.c
@@ -344,11 +344,6 @@ void netdata_cleanup_and_exit(int ret) {
webrtc_close_all_connections();
- delta_shutdown_time("disable ML detection and training threads");
-
- ml_stop_threads();
- ml_fini();
-
delta_shutdown_time("disable maintenance, new queries, new web requests, new streaming connections and aclk");
service_signal_exit(
@@ -377,6 +372,11 @@ void netdata_cleanup_and_exit(int ret) {
| SERVICE_STREAMING
, 3 * USEC_PER_SEC);
+ delta_shutdown_time("disable ML detection and training threads");
+
+ ml_stop_threads();
+ ml_fini();
+
delta_shutdown_time("stop context thread");
timeout = !service_wait_exit(
diff --git a/database/sqlite/sqlite_metadata.c b/database/sqlite/sqlite_metadata.c
index 607d789a58..4f115c7d81 100644
--- a/database/sqlite/sqlite_metadata.c
+++ b/database/sqlite/sqlite_metadata.c
@@ -1485,7 +1485,6 @@ static inline void queue_metadata_cmd(enum metadata_opcode opcode, const void *p
cmd.param[1] = param1;
cmd.completion = NULL;
metadata_enq_cmd(&metasync_worker, &cmd);
-
}
// Public
diff --git a/ml/ad_charts.cc b/ml/ad_charts.cc
index bd065cfcc4..ca4dca1393 100644
--- a/ml/ad_charts.cc
+++ b/ml/ad_charts.cc
@@ -183,6 +183,41 @@ void ml_update_dimensions_chart(ml_host_t *host, const ml_machine_learning_stats
rrdset_done(host->dimensions_rs);
}
+
+ // ML running
+ {
+ if (!host->ml_running_rs) {
+ char id_buf[1024];
+ char name_buf[1024];
+
+ snprintfz(id_buf, 1024, "ml_running_on_%s", localhost->machine_guid);
+ snprintfz(name_buf, 1024, "ml_running_on_%s", rrdhost_hostname(localhost));
+
+ host->ml_running_rs = rrdset_create(
+ host->rh,
+ "anomaly_detection", // type
+ id_buf, // id
+ name_buf, // name
+ "anomaly_detection", // family
+ "anomaly_detection.ml_running", // ctx
+ "ML running", // title
+ "boolean", // units
+ NETDATA_ML_PLUGIN, // plugin
+ NETDATA_ML_MODULE_DETECTION, // module
+ NETDATA_ML_CHART_RUNNING, // priority
+ localhost->rrd_update_every, // update_every
+ RRDSET_TYPE_LINE // chart_type
+ );
+ rrdset_flag_set(host->ml_running_rs, RRDSET_FLAG_ANOMALY_DETECTION);
+
+ host->ml_running_rd =
+ rrddim_add(host->ml_running_rs, "ml_running", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ }
+
+ rrddim_set_by_pointer(host->ml_running_rs,
+ host->ml_running_rd, host->ml_running);
+ rrdset_done(host->ml_running_rs);
+ }
}
void ml_update_host_and_detection_rate_charts(ml_host_t *host, collected_number AnomalyRate) {
@@ -260,47 +295,55 @@ void ml_update_host_and_detection_rate_charts(ml_host_t *host, collected_number
/*
* Compute the values of the dimensions based on the host rate chart
*/
- ONEWAYALLOC *OWA = onewayalloc_create(0);
- time_t Now = now_realtime_sec();
- time_t Before = Now - host->rh->rrd_update_every;
- time_t After = Before - Cfg.anomaly_detection_query_duration;
- RRDR_OPTIONS Options = static_cast<RRDR_OPTIONS>(0x00000000);
+ if (host->ml_running) {
+ ONEWAYALLOC *OWA = onewayalloc_create(0);
+ time_t Now = now_realtime_sec();
+ time_t Before = Now - host->rh->rrd_update_every;
+ time_t After = Before - Cfg.anomaly_detection_query_duration;
+ RRDR_OPTIONS Options = static_cast<RRDR_OPTIONS>(0x00000000);
- RRDR *R = rrd2rrdr_legacy(
- OWA,
- host->anomaly_rate_rs,
- 1 /* points wanted */,
- After,
- Before,
- Cfg.anomaly_detection_grouping_method,
- 0 /* resampling time */,
- Options, "anomaly_rate",
- NULL /* group options */,
- 0, /* timeout */
- 0, /* tier */
- QUERY_SOURCE_ML,
- STORAGE_PRIORITY_SYNCHRONOUS
- );
+ RRDR *R = rrd2rrdr_legacy(
+ OWA,
+ host->anomaly_rate_rs,
+ 1 /* points wanted */,
+ After,
+ Before,
+ Cfg.anomaly_detection_grouping_method,
+ 0 /* resampling time */,
+ Options, "anomaly_rate",
+ NULL /* group options */,
+ 0, /* timeout */
+ 0, /* tier */
+ QUERY_SOURCE_ML,
+ STORAGE_PRIORITY_SYNCHRONOUS
+ );
- if (R) {
- if (R->d == 1 && R->n == 1 && R->rows == 1) {
- static thread_local bool prev_above_threshold = false;
- bool above_threshold = R->v[0] >= Cfg.host_anomaly_rate_threshold;
- bool new_anomaly_event = above_threshold && !prev_above_threshold;
- prev_above_threshold = above_threshold;
+ if (R) {
+ if (R->d == 1 && R->n == 1 && R->rows == 1) {
+ static thread_local bool prev_above_threshold = false;
+ bool above_threshold = R->v[0] >= Cfg.host_anomaly_rate_threshold;
+ bool new_anomaly_event = above_threshold && !prev_above_threshold;
+ prev_above_threshold = above_threshold;
- rrddim_set_by_pointer(host->detector_events_rs,
- host->detector_events_above_threshold_rd, above_threshold);
- rrddim_set_by_pointer(host->detector_events_rs,
- host->detector_events_new_anomaly_event_rd, new_anomaly_event);
+ rrddim_set_by_pointer(host->detector_events_rs,
+ host->detector_events_above_threshold_rd, above_threshold);
+ rrddim_set_by_pointer(host->detector_events_rs,
+ host->detector_events_new_anomaly_event_rd, new_anomaly_event);
- rrdset_done(host->detector_events_rs);
+ rrdset_done(host->detector_events_rs);
+ }
+
+ rrdr_free(OWA, R);
}
- rrdr_free(OWA, R);
+ onewayalloc_destroy(OWA);
+ } else {
+ rrddim_set_by_pointer(host->detector_events_rs,
+ host->detector_events_above_threshold_rd, 0);
+ rrddim_set_by_pointer(host->detector_events_rs,
+ host->detector_events_new_anomaly_event_rd, 0);
+ rrdset_done(host->detector_events_rs);
}
-
- onewayalloc_destroy(OWA);
}
}
diff --git a/ml/ml-dummy.c b/ml/ml-dummy.c
index 708ab68ead..4c7026b222 100644
--- a/ml/ml-dummy.c
+++ b/ml/ml-dummy.c
@@ -33,6 +33,14 @@ void ml_host_delete(RRDHOST *rh) {
UNUSED(rh);
}
+void ml_host_start(RRDHOST *rh) {
+ UNUSED(rh);
+}
+
+void ml_host_stop(RRDHOST *rh) {
+ UNUSED(rh);
+}
+
void ml_host_start_training_thread(RRDHOST *rh) {
UNUSED(rh);
}
diff --git a/ml/ml-private.h b/ml/ml-private.h
index 2ed70d1ca9..f0e2e7eafe 100644
--- a/ml/ml-private.h
+++ b/ml/ml-private.h
@@ -195,7 +195,7 @@ typedef struct {
std::vector<calculated_number_t> cns;
std::vector<ml_kmeans_t> km_contexts;
- netdata_mutex_t mutex;
+ SPINLOCK slock;
ml_kmeans_t kmeans;
std::vector<DSample> feature;
@@ -206,8 +206,6 @@ typedef struct {
typedef struct {
RRDSET *rs;
ml_machine_learning_stats_t mls;
-
- netdata_mutex_t mutex;
} ml_chart_t;
void ml_chart_update_dimension(ml_chart_t *chart, ml_dimension_t *dim, bool is_anomalous);
@@ -215,6 +213,8 @@ void ml_chart_update_dimension(ml_chart_t *chart, ml_dimension_t *dim, bool is_a
typedef struct {
RRDHOST *rh;
+ std::atomic<bool> ml_running;
+
ml_machine_learning_stats_t mls;
calculated_number_t host_anomaly_rate;
@@ -227,6 +227,9 @@ typedef struct {
* bookkeeping for anomaly detection charts
*/
+ RRDSET *ml_running_rs;
+ RRDDIM *ml_running_rd;
+
RRDSET *machine_learning_status_rs;
RRDDIM *machine_learning_status_enabled_rd;
RRDDIM *machine_learning_status_disabled_sp_rd;
diff --git a/ml/ml.cc b/ml/ml.cc
index a5f0fa062d..51fa9ecd23 100644
--- a/ml/ml.cc
+++ b/ml/ml.cc
@@ -568,9 +568,9 @@ int ml_dimension_load_models(RRDDIM *rd) {
if (!dim)
return 0;
- netdata_mutex_lock(&dim->mutex);
+ netdata_spinlock_lock(&dim->slock);
bool is_empty = dim->km_contexts.empty();
- netdata_mutex_unlock(&dim->mutex);
+ netdata_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);
+ netdata_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);
+ netdata_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);
+ netdata_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);
+
+ netdata_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);
+ netdata_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);
+ netdata_spinlock_unlock(&dim->slock);
}
return training_response.result;
@@ -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 (netdata_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);
+ netdata_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);
+ netdata_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);
+ netdata_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;
+
+ netdata_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);
+
+ netdata_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 +
@@ -1309,8 +1376,6 @@ 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 +1387,6 @@ 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 +1397,6 @@ bool ml_chart_update_begin(RRDSET *rs)
if (!chart)
return false;
- netdata_mutex_lock(&chart->mutex);
chart->mls = {};
return true;
}
@@ -1344,8 +1406,6 @@ 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 +1420,9 @@ 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 +1431,7 @@ void ml_dimension_new(RRDDIM *rd)
else
dim->mls = MACHINE_LEARNING_STATUS_ENABLED;
- netdata_mutex_init(&dim->mutex);
+ netdata_spinlock_init(&dim->slock);
dim->km_contexts.reserve(Cfg.num_models_to_use);
@@ -1385,8 +1446,6 @@ void ml_dimension_delete(RRDDIM *rd)
if (!dim)
return;
- netdata_mutex_destroy(&dim->mutex);
-
delete dim;
rd->ml_dimension = NULL;
}
@@ -1397,6 +1456,10 @@ 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);
diff --git a/ml/ml.h b/ml/ml.h
index 964dd08214..6ba70bfdaa 100644
--- a/ml/ml.h
+++ b/ml/ml.h
@@ -23,6 +23,9 @@ void ml_stop_threads(void);
void ml_host_new(RRDHOST *rh);
void ml_host_delete(RRDHOST *rh);
+void ml_host_start(RRDHOST *RH);
+void ml_host_stop(RRDHOST *RH);
+
void ml_host_get_info(RRDHOST *RH, BUFFER *wb);
void ml_host_get_detection_info(RRDHOST *RH, BUFFER *wb);
void ml_host_get_models(RRDHOST *RH, BUFFER *wb);