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authorDaniel Baumann <daniel.baumann@progress-linux.org>2023-05-08 16:27:08 +0000
committerDaniel Baumann <daniel.baumann@progress-linux.org>2023-05-08 16:27:08 +0000
commit81581f9719bc56f01d5aa08952671d65fda9867a (patch)
tree0f5c6b6138bf169c23c9d24b1fc0a3521385cb18 /ml/ad_charts.cc
parentReleasing debian version 1.38.1-1. (diff)
downloadnetdata-81581f9719bc56f01d5aa08952671d65fda9867a.tar.xz
netdata-81581f9719bc56f01d5aa08952671d65fda9867a.zip
Merging upstream version 1.39.0.
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
Diffstat (limited to 'ml/ad_charts.cc')
-rw-r--r--ml/ad_charts.cc475
1 files changed, 475 insertions, 0 deletions
diff --git a/ml/ad_charts.cc b/ml/ad_charts.cc
new file mode 100644
index 000000000..086cd5aa0
--- /dev/null
+++ b/ml/ad_charts.cc
@@ -0,0 +1,475 @@
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+#include "ad_charts.h"
+
+void ml_update_dimensions_chart(ml_host_t *host, const ml_machine_learning_stats_t &mls) {
+ /*
+ * Machine learning status
+ */
+ if (Cfg.enable_statistics_charts) {
+ if (!host->machine_learning_status_rs) {
+ char id_buf[1024];
+ char name_buf[1024];
+
+ snprintfz(id_buf, 1024, "machine_learning_status_on_%s", localhost->machine_guid);
+ snprintfz(name_buf, 1024, "machine_learning_status_on_%s", rrdhost_hostname(localhost));
+
+ host->machine_learning_status_rs = rrdset_create(
+ host->rh,
+ "netdata", // type
+ id_buf,
+ name_buf, // name
+ NETDATA_ML_CHART_FAMILY, // family
+ "netdata.machine_learning_status", // ctx
+ "Machine learning status", // title
+ "dimensions", // units
+ NETDATA_ML_PLUGIN, // plugin
+ NETDATA_ML_MODULE_TRAINING, // module
+ NETDATA_ML_CHART_PRIO_MACHINE_LEARNING_STATUS, // priority
+ localhost->rrd_update_every, // update_every
+ RRDSET_TYPE_LINE // chart_type
+ );
+ rrdset_flag_set(host->machine_learning_status_rs , RRDSET_FLAG_ANOMALY_DETECTION);
+
+ host->machine_learning_status_enabled_rd =
+ rrddim_add(host->machine_learning_status_rs, "enabled", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ host->machine_learning_status_disabled_sp_rd =
+ rrddim_add(host->machine_learning_status_rs, "disabled-sp", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ }
+
+ rrddim_set_by_pointer(host->machine_learning_status_rs,
+ host->machine_learning_status_enabled_rd, mls.num_machine_learning_status_enabled);
+ rrddim_set_by_pointer(host->machine_learning_status_rs,
+ host->machine_learning_status_disabled_sp_rd, mls.num_machine_learning_status_disabled_sp);
+
+ rrdset_done(host->machine_learning_status_rs);
+ }
+
+ /*
+ * Metric type
+ */
+ if (Cfg.enable_statistics_charts) {
+ if (!host->metric_type_rs) {
+ char id_buf[1024];
+ char name_buf[1024];
+
+ snprintfz(id_buf, 1024, "metric_types_on_%s", localhost->machine_guid);
+ snprintfz(name_buf, 1024, "metric_types_on_%s", rrdhost_hostname(localhost));
+
+ host->metric_type_rs = rrdset_create(
+ host->rh,
+ "netdata", // type
+ id_buf, // id
+ name_buf, // name
+ NETDATA_ML_CHART_FAMILY, // family
+ "netdata.metric_types", // ctx
+ "Dimensions by metric type", // title
+ "dimensions", // units
+ NETDATA_ML_PLUGIN, // plugin
+ NETDATA_ML_MODULE_TRAINING, // module
+ NETDATA_ML_CHART_PRIO_METRIC_TYPES, // priority
+ localhost->rrd_update_every, // update_every
+ RRDSET_TYPE_LINE // chart_type
+ );
+ rrdset_flag_set(host->metric_type_rs, RRDSET_FLAG_ANOMALY_DETECTION);
+
+ host->metric_type_constant_rd =
+ rrddim_add(host->metric_type_rs, "constant", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ host->metric_type_variable_rd =
+ rrddim_add(host->metric_type_rs, "variable", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ }
+
+ rrddim_set_by_pointer(host->metric_type_rs,
+ host->metric_type_constant_rd, mls.num_metric_type_constant);
+ rrddim_set_by_pointer(host->metric_type_rs,
+ host->metric_type_variable_rd, mls.num_metric_type_variable);
+
+ rrdset_done(host->metric_type_rs);
+ }
+
+ /*
+ * Training status
+ */
+ if (Cfg.enable_statistics_charts) {
+ if (!host->training_status_rs) {
+ char id_buf[1024];
+ char name_buf[1024];
+
+ snprintfz(id_buf, 1024, "training_status_on_%s", localhost->machine_guid);
+ snprintfz(name_buf, 1024, "training_status_on_%s", rrdhost_hostname(localhost));
+
+ host->training_status_rs = rrdset_create(
+ host->rh,
+ "netdata", // type
+ id_buf, // id
+ name_buf, // name
+ NETDATA_ML_CHART_FAMILY, // family
+ "netdata.training_status", // ctx
+ "Training status of dimensions", // title
+ "dimensions", // units
+ NETDATA_ML_PLUGIN, // plugin
+ NETDATA_ML_MODULE_TRAINING, // module
+ NETDATA_ML_CHART_PRIO_TRAINING_STATUS, // priority
+ localhost->rrd_update_every, // update_every
+ RRDSET_TYPE_LINE // chart_type
+ );
+
+ rrdset_flag_set(host->training_status_rs, RRDSET_FLAG_ANOMALY_DETECTION);
+
+ host->training_status_untrained_rd =
+ rrddim_add(host->training_status_rs, "untrained", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ host->training_status_pending_without_model_rd =
+ rrddim_add(host->training_status_rs, "pending-without-model", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ host->training_status_trained_rd =
+ rrddim_add(host->training_status_rs, "trained", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ host->training_status_pending_with_model_rd =
+ rrddim_add(host->training_status_rs, "pending-with-model", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ }
+
+ rrddim_set_by_pointer(host->training_status_rs,
+ host->training_status_untrained_rd, mls.num_training_status_untrained);
+ rrddim_set_by_pointer(host->training_status_rs,
+ host->training_status_pending_without_model_rd, mls.num_training_status_pending_without_model);
+ rrddim_set_by_pointer(host->training_status_rs,
+ host->training_status_trained_rd, mls.num_training_status_trained);
+ rrddim_set_by_pointer(host->training_status_rs,
+ host->training_status_pending_with_model_rd, mls.num_training_status_pending_with_model);
+
+ rrdset_done(host->training_status_rs);
+ }
+
+ /*
+ * Prediction status
+ */
+ {
+ if (!host->dimensions_rs) {
+ char id_buf[1024];
+ char name_buf[1024];
+
+ snprintfz(id_buf, 1024, "dimensions_on_%s", localhost->machine_guid);
+ snprintfz(name_buf, 1024, "dimensions_on_%s", rrdhost_hostname(localhost));
+
+ host->dimensions_rs = rrdset_create(
+ host->rh,
+ "anomaly_detection", // type
+ id_buf, // id
+ name_buf, // name
+ "dimensions", // family
+ "anomaly_detection.dimensions", // ctx
+ "Anomaly detection dimensions", // title
+ "dimensions", // units
+ NETDATA_ML_PLUGIN, // plugin
+ NETDATA_ML_MODULE_TRAINING, // module
+ ML_CHART_PRIO_DIMENSIONS, // priority
+ localhost->rrd_update_every, // update_every
+ RRDSET_TYPE_LINE // chart_type
+ );
+ rrdset_flag_set(host->dimensions_rs, RRDSET_FLAG_ANOMALY_DETECTION);
+
+ host->dimensions_anomalous_rd =
+ rrddim_add(host->dimensions_rs, "anomalous", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ host->dimensions_normal_rd =
+ rrddim_add(host->dimensions_rs, "normal", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ }
+
+ rrddim_set_by_pointer(host->dimensions_rs,
+ host->dimensions_anomalous_rd, mls.num_anomalous_dimensions);
+ rrddim_set_by_pointer(host->dimensions_rs,
+ host->dimensions_normal_rd, mls.num_normal_dimensions);
+
+ rrdset_done(host->dimensions_rs);
+ }
+}
+
+void ml_update_host_and_detection_rate_charts(ml_host_t *host, collected_number AnomalyRate) {
+ /*
+ * Anomaly rate
+ */
+ {
+ if (!host->anomaly_rate_rs) {
+ char id_buf[1024];
+ char name_buf[1024];
+
+ snprintfz(id_buf, 1024, "anomaly_rate_on_%s", localhost->machine_guid);
+ snprintfz(name_buf, 1024, "anomaly_rate_on_%s", rrdhost_hostname(localhost));
+
+ host->anomaly_rate_rs = rrdset_create(
+ host->rh,
+ "anomaly_detection", // type
+ id_buf, // id
+ name_buf, // name
+ "anomaly_rate", // family
+ "anomaly_detection.anomaly_rate", // ctx
+ "Percentage of anomalous dimensions", // title
+ "percentage", // units
+ NETDATA_ML_PLUGIN, // plugin
+ NETDATA_ML_MODULE_DETECTION, // module
+ ML_CHART_PRIO_ANOMALY_RATE, // priority
+ localhost->rrd_update_every, // update_every
+ RRDSET_TYPE_LINE // chart_type
+ );
+ rrdset_flag_set(host->anomaly_rate_rs, RRDSET_FLAG_ANOMALY_DETECTION);
+
+ host->anomaly_rate_rd =
+ rrddim_add(host->anomaly_rate_rs, "anomaly_rate", NULL, 1, 100, RRD_ALGORITHM_ABSOLUTE);
+ }
+
+ rrddim_set_by_pointer(host->anomaly_rate_rs, host->anomaly_rate_rd, AnomalyRate);
+
+ rrdset_done(host->anomaly_rate_rs);
+ }
+
+ /*
+ * Detector Events
+ */
+ {
+ if (!host->detector_events_rs) {
+ char id_buf[1024];
+ char name_buf[1024];
+
+ snprintfz(id_buf, 1024, "anomaly_detection_on_%s", localhost->machine_guid);
+ snprintfz(name_buf, 1024, "anomaly_detection_on_%s", rrdhost_hostname(localhost));
+
+ host->detector_events_rs = rrdset_create(
+ host->rh,
+ "anomaly_detection", // type
+ id_buf, // id
+ name_buf, // name
+ "anomaly_detection", // family
+ "anomaly_detection.detector_events", // ctx
+ "Anomaly detection events", // title
+ "percentage", // units
+ NETDATA_ML_PLUGIN, // plugin
+ NETDATA_ML_MODULE_DETECTION, // module
+ ML_CHART_PRIO_DETECTOR_EVENTS, // priority
+ localhost->rrd_update_every, // update_every
+ RRDSET_TYPE_LINE // chart_type
+ );
+ rrdset_flag_set(host->detector_events_rs, RRDSET_FLAG_ANOMALY_DETECTION);
+
+ host->detector_events_above_threshold_rd =
+ rrddim_add(host->detector_events_rs, "above_threshold", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ host->detector_events_new_anomaly_event_rd =
+ rrddim_add(host->detector_events_rs, "new_anomaly_event", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ }
+
+ /*
+ * 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);
+
+ 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;
+
+ 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);
+ }
+
+ rrdr_free(OWA, R);
+ }
+
+ onewayalloc_destroy(OWA);
+ }
+}
+
+void ml_update_training_statistics_chart(ml_training_thread_t *training_thread, const ml_training_stats_t &ts) {
+ /*
+ * queue stats
+ */
+ {
+ if (!training_thread->queue_stats_rs) {
+ char id_buf[1024];
+ char name_buf[1024];
+
+ snprintfz(id_buf, 1024, "training_queue_%zu_stats", training_thread->id);
+ snprintfz(name_buf, 1024, "training_queue_%zu_stats", training_thread->id);
+
+ training_thread->queue_stats_rs = rrdset_create(
+ localhost,
+ "netdata", // type
+ id_buf, // id
+ name_buf, // name
+ NETDATA_ML_CHART_FAMILY, // family
+ "netdata.queue_stats", // ctx
+ "Training queue stats", // title
+ "items", // units
+ NETDATA_ML_PLUGIN, // plugin
+ NETDATA_ML_MODULE_TRAINING, // module
+ NETDATA_ML_CHART_PRIO_QUEUE_STATS, // priority
+ localhost->rrd_update_every, // update_every
+ RRDSET_TYPE_LINE// chart_type
+ );
+ rrdset_flag_set(training_thread->queue_stats_rs, RRDSET_FLAG_ANOMALY_DETECTION);
+
+ training_thread->queue_stats_queue_size_rd =
+ rrddim_add(training_thread->queue_stats_rs, "queue_size", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ training_thread->queue_stats_popped_items_rd =
+ rrddim_add(training_thread->queue_stats_rs, "popped_items", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ }
+
+ rrddim_set_by_pointer(training_thread->queue_stats_rs,
+ training_thread->queue_stats_queue_size_rd, ts.queue_size);
+ rrddim_set_by_pointer(training_thread->queue_stats_rs,
+ training_thread->queue_stats_popped_items_rd, ts.num_popped_items);
+
+ rrdset_done(training_thread->queue_stats_rs);
+ }
+
+ /*
+ * training stats
+ */
+ {
+ if (!training_thread->training_time_stats_rs) {
+ char id_buf[1024];
+ char name_buf[1024];
+
+ snprintfz(id_buf, 1024, "training_queue_%zu_time_stats", training_thread->id);
+ snprintfz(name_buf, 1024, "training_queue_%zu_time_stats", training_thread->id);
+
+ training_thread->training_time_stats_rs = rrdset_create(
+ localhost,
+ "netdata", // type
+ id_buf, // id
+ name_buf, // name
+ NETDATA_ML_CHART_FAMILY, // family
+ "netdata.training_time_stats", // ctx
+ "Training time stats", // title
+ "milliseconds", // units
+ NETDATA_ML_PLUGIN, // plugin
+ NETDATA_ML_MODULE_TRAINING, // module
+ NETDATA_ML_CHART_PRIO_TRAINING_TIME_STATS, // priority
+ localhost->rrd_update_every, // update_every
+ RRDSET_TYPE_LINE// chart_type
+ );
+ rrdset_flag_set(training_thread->training_time_stats_rs, RRDSET_FLAG_ANOMALY_DETECTION);
+
+ training_thread->training_time_stats_allotted_rd =
+ rrddim_add(training_thread->training_time_stats_rs, "allotted", NULL, 1, 1000, RRD_ALGORITHM_ABSOLUTE);
+ training_thread->training_time_stats_consumed_rd =
+ rrddim_add(training_thread->training_time_stats_rs, "consumed", NULL, 1, 1000, RRD_ALGORITHM_ABSOLUTE);
+ training_thread->training_time_stats_remaining_rd =
+ rrddim_add(training_thread->training_time_stats_rs, "remaining", NULL, 1, 1000, RRD_ALGORITHM_ABSOLUTE);
+ }
+
+ rrddim_set_by_pointer(training_thread->training_time_stats_rs,
+ training_thread->training_time_stats_allotted_rd, ts.allotted_ut);
+ rrddim_set_by_pointer(training_thread->training_time_stats_rs,
+ training_thread->training_time_stats_consumed_rd, ts.consumed_ut);
+ rrddim_set_by_pointer(training_thread->training_time_stats_rs,
+ training_thread->training_time_stats_remaining_rd, ts.remaining_ut);
+
+ rrdset_done(training_thread->training_time_stats_rs);
+ }
+
+ /*
+ * training result stats
+ */
+ {
+ if (!training_thread->training_results_rs) {
+ char id_buf[1024];
+ char name_buf[1024];
+
+ snprintfz(id_buf, 1024, "training_queue_%zu_results", training_thread->id);
+ snprintfz(name_buf, 1024, "training_queue_%zu_results", training_thread->id);
+
+ training_thread->training_results_rs = rrdset_create(
+ localhost,
+ "netdata", // type
+ id_buf, // id
+ name_buf, // name
+ NETDATA_ML_CHART_FAMILY, // family
+ "netdata.training_results", // ctx
+ "Training results", // title
+ "events", // units
+ NETDATA_ML_PLUGIN, // plugin
+ NETDATA_ML_MODULE_TRAINING, // module
+ NETDATA_ML_CHART_PRIO_TRAINING_RESULTS, // priority
+ localhost->rrd_update_every, // update_every
+ RRDSET_TYPE_LINE// chart_type
+ );
+ rrdset_flag_set(training_thread->training_results_rs, RRDSET_FLAG_ANOMALY_DETECTION);
+
+ training_thread->training_results_ok_rd =
+ rrddim_add(training_thread->training_results_rs, "ok", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ training_thread->training_results_invalid_query_time_range_rd =
+ rrddim_add(training_thread->training_results_rs, "invalid-queries", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ training_thread->training_results_not_enough_collected_values_rd =
+ rrddim_add(training_thread->training_results_rs, "not-enough-values", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ training_thread->training_results_null_acquired_dimension_rd =
+ rrddim_add(training_thread->training_results_rs, "null-acquired-dimensions", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ training_thread->training_results_chart_under_replication_rd =
+ rrddim_add(training_thread->training_results_rs, "chart-under-replication", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
+ }
+
+ rrddim_set_by_pointer(training_thread->training_results_rs,
+ training_thread->training_results_ok_rd, ts.training_result_ok);
+ rrddim_set_by_pointer(training_thread->training_results_rs,
+ training_thread->training_results_invalid_query_time_range_rd, ts.training_result_invalid_query_time_range);
+ rrddim_set_by_pointer(training_thread->training_results_rs,
+ training_thread->training_results_not_enough_collected_values_rd, ts.training_result_not_enough_collected_values);
+ rrddim_set_by_pointer(training_thread->training_results_rs,
+ training_thread->training_results_null_acquired_dimension_rd, ts.training_result_null_acquired_dimension);
+ rrddim_set_by_pointer(training_thread->training_results_rs,
+ training_thread->training_results_chart_under_replication_rd, ts.training_result_chart_under_replication);
+
+ rrdset_done(training_thread->training_results_rs);
+ }
+}
+
+void ml_update_global_statistics_charts(uint64_t models_consulted) {
+ if (Cfg.enable_statistics_charts) {
+ static RRDSET *st = NULL;
+ static RRDDIM *rd = NULL;
+
+ if (unlikely(!st)) {
+ st = rrdset_create_localhost(
+ "netdata" // type
+ , "ml_models_consulted" // id
+ , NULL // name
+ , NETDATA_ML_CHART_FAMILY // family
+ , NULL // context
+ , "KMeans models used for prediction" // title
+ , "models" // units
+ , NETDATA_ML_PLUGIN // plugin
+ , NETDATA_ML_MODULE_DETECTION // module
+ , NETDATA_ML_CHART_PRIO_MACHINE_LEARNING_STATUS // priority
+ , localhost->rrd_update_every // update_every
+ , RRDSET_TYPE_AREA // chart_type
+ );
+
+ rd = rrddim_add(st, "num_models_consulted", NULL, 1, 1, RRD_ALGORITHM_INCREMENTAL);
+ }
+
+ rrddim_set_by_pointer(st, rd, (collected_number) models_consulted);
+
+ rrdset_done(st);
+ }
+}