diff options
| -rw-r--r-- | daemon/global_statistics.c | 28 | ||||
| -rw-r--r-- | daemon/main.c | 16 | ||||
| -rw-r--r-- | daemon/main.h | 20 | ||||
| -rw-r--r-- | database/rrdhost.c | 3 | ||||
| -rw-r--r-- | ml/Config.cc | 12 | ||||
| -rw-r--r-- | ml/ad_charts.cc | 167 | ||||
| -rw-r--r-- | ml/ad_charts.h | 2 | ||||
| -rw-r--r-- | ml/ml-dummy.c | 6 | ||||
| -rw-r--r-- | ml/ml-private.h | 36 | ||||
| -rw-r--r-- | ml/ml.cc | 546 | ||||
| -rw-r--r-- | ml/ml.h | 8 |
11 files changed, 408 insertions, 436 deletions
diff --git a/daemon/global_statistics.c b/daemon/global_statistics.c index ee68bebd15..0dc3ee6452 100644 --- a/daemon/global_statistics.c +++ b/daemon/global_statistics.c @@ -827,7 +827,33 @@ static void global_statistics_charts(void) { rrdset_done(st_points_stored); } - ml_update_global_statistics_charts(gs.ml_models_consulted); + { + 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) gs.ml_models_consulted); + + rrdset_done(st); + } } // ---------------------------------------------------------------------------- diff --git a/daemon/main.c b/daemon/main.c index 3a3b70782f..85d2996ef9 100644 --- a/daemon/main.c +++ b/daemon/main.c @@ -148,6 +148,10 @@ static void service_to_buffer(BUFFER *wb, SERVICE_TYPE service) { buffer_strcat(wb, "MAINTENANCE "); if(service & SERVICE_COLLECTORS) buffer_strcat(wb, "COLLECTORS "); + if(service & SERVICE_ML_TRAINING) + buffer_strcat(wb, "ML_TRAINING "); + if(service & SERVICE_ML_PREDICTION) + buffer_strcat(wb, "ML_PREDICTION "); if(service & SERVICE_REPLICATION) buffer_strcat(wb, "REPLICATION "); if(service & ABILITY_DATA_QUERIES) @@ -336,10 +340,6 @@ void netdata_cleanup_and_exit(int ret) { } #endif - delta_shutdown_time("disable ML detection and training threads"); - - ml_fini(); - delta_shutdown_time("disable maintenance, new queries, new web requests, new streaming connections and aclk"); service_signal_exit( @@ -351,11 +351,12 @@ void netdata_cleanup_and_exit(int ret) { | SERVICE_ACLKSYNC ); - delta_shutdown_time("stop replication, exporters, health and web servers threads"); + delta_shutdown_time("stop replication, exporters, ML training, health and web servers threads"); timeout = !service_wait_exit( SERVICE_REPLICATION | SERVICE_EXPORTERS + | SERVICE_ML_TRAINING | SERVICE_HEALTH | SERVICE_WEB_SERVER , 3 * USEC_PER_SEC); @@ -367,10 +368,11 @@ void netdata_cleanup_and_exit(int ret) { | SERVICE_STREAMING , 3 * USEC_PER_SEC); - delta_shutdown_time("stop context thread"); + delta_shutdown_time("stop ML prediction and context threads"); timeout = !service_wait_exit( - SERVICE_CONTEXT + SERVICE_ML_PREDICTION + | SERVICE_CONTEXT , 3 * USEC_PER_SEC); delta_shutdown_time("stop maintenance thread"); diff --git a/daemon/main.h b/daemon/main.h index 3e32c5ad6d..7e659e939a 100644 --- a/daemon/main.h +++ b/daemon/main.h @@ -33,15 +33,17 @@ typedef enum { ABILITY_STREAMING_CONNECTIONS = (1 << 2), SERVICE_MAINTENANCE = (1 << 3), SERVICE_COLLECTORS = (1 << 4), - SERVICE_REPLICATION = (1 << 5), - SERVICE_WEB_SERVER = (1 << 6), - SERVICE_ACLK = (1 << 7), - SERVICE_HEALTH = (1 << 8), - SERVICE_STREAMING = (1 << 9), - SERVICE_CONTEXT = (1 << 10), - SERVICE_ANALYTICS = (1 << 11), - SERVICE_EXPORTERS = (1 << 12), - SERVICE_ACLKSYNC = (1 << 13) + SERVICE_ML_TRAINING = (1 << 5), + SERVICE_ML_PREDICTION = (1 << 6), + SERVICE_REPLICATION = (1 << 7), + SERVICE_WEB_SERVER = (1 << 8), + SERVICE_ACLK = (1 << 9), + SERVICE_HEALTH = (1 << 10), + SERVICE_STREAMING = (1 << 11), + SERVICE_CONTEXT = (1 << 12), + SERVICE_ANALYTICS = (1 << 13), + SERVICE_EXPORTERS = (1 << 14), + SERVICE_ACLKSYNC = (1 << 15) } SERVICE_TYPE; typedef enum { diff --git a/database/rrdhost.c b/database/rrdhost.c index 24593e395a..2917b0977b 100644 --- a/database/rrdhost.c +++ b/database/rrdhost.c @@ -524,6 +524,7 @@ int is_legacy = 1; rrdhost_load_rrdcontext_data(host); // rrdhost_flag_set(host, RRDHOST_FLAG_METADATA_INFO | RRDHOST_FLAG_METADATA_UPDATE); ml_host_new(host); + ml_host_start_training_thread(host); } else rrdhost_flag_set(host, RRDHOST_FLAG_PENDING_CONTEXT_LOAD | RRDHOST_FLAG_ARCHIVED | RRDHOST_FLAG_ORPHAN); @@ -640,6 +641,7 @@ static void rrdhost_update(RRDHOST *host host->rrdpush_replication_step = rrdpush_replication_step; ml_host_new(host); + ml_host_start_training_thread(host); rrdhost_load_rrdcontext_data(host); info("Host %s is not in archived mode anymore", rrdhost_hostname(host)); @@ -1141,6 +1143,7 @@ void rrdhost_free___while_having_rrd_wrlock(RRDHOST *host, bool force) { rrdcalctemplate_index_destroy(host); // cleanup ML resources + ml_host_stop_training_thread(host); ml_host_delete(host); freez(host->exporting_flags); diff --git a/ml/Config.cc b/ml/Config.cc index 415d11b838..8b04590d77 100644 --- a/ml/Config.cc +++ b/ml/Config.cc @@ -34,7 +34,7 @@ void ml_config_load(ml_config_t *cfg) { unsigned smooth_n = config_get_number(config_section_ml, "num samples to smooth", 3); unsigned lag_n = config_get_number(config_section_ml, "num samples to lag", 5); - double random_sampling_ratio = config_get_float(config_section_ml, "random sampling ratio", 1.0 / 5.0 /* default lag_n */); + double random_sampling_ratio = config_get_float(config_section_ml, "random sampling ratio", 1.0 / lag_n); unsigned max_kmeans_iters = config_get_number(config_section_ml, "maximum number of k-means iterations", 1000); double dimension_anomaly_rate_threshold = config_get_float(config_section_ml, "dimension anomaly score threshold", 0.99); @@ -43,10 +43,6 @@ void ml_config_load(ml_config_t *cfg) { std::string anomaly_detection_grouping_method = config_get(config_section_ml, "anomaly detection grouping method", "average"); time_t anomaly_detection_query_duration = config_get_number(config_section_ml, "anomaly detection grouping duration", 5 * 60); - size_t num_training_threads = config_get_number(config_section_ml, "num training threads", 4); - - bool enable_statistics_charts = config_get_boolean(config_section_ml, "enable statistics charts", false); - /* * Clamp */ @@ -68,8 +64,6 @@ void ml_config_load(ml_config_t *cfg) { host_anomaly_rate_threshold = clamp(host_anomaly_rate_threshold, 0.1, 10.0); anomaly_detection_query_duration = clamp<time_t>(anomaly_detection_query_duration, 60, 15 * 60); - num_training_threads = clamp<size_t>(num_training_threads, 1, 128); - /* * Validate */ @@ -115,8 +109,4 @@ void ml_config_load(ml_config_t *cfg) { cfg->sp_charts_to_skip = simple_pattern_create(cfg->charts_to_skip.c_str(), NULL, SIMPLE_PATTERN_EXACT, true); cfg->stream_anomaly_detection_charts = config_get_boolean(config_section_ml, "stream anomaly detection charts", true); - - cfg->num_training_threads = num_training_threads; - - cfg->enable_statistics_charts = enable_statistics_charts; } diff --git a/ml/ad_charts.cc b/ml/ad_charts.cc index 086cd5aa02..a32ff6c650 100644 --- a/ml/ad_charts.cc +++ b/ml/ad_charts.cc @@ -6,7 +6,7 @@ void ml_update_dimensions_chart(ml_host_t *host, const ml_machine_learning_stats /* * Machine learning status */ - if (Cfg.enable_statistics_charts) { + { if (!host->machine_learning_status_rs) { char id_buf[1024]; char name_buf[1024]; @@ -48,7 +48,7 @@ void ml_update_dimensions_chart(ml_host_t *host, const ml_machine_learning_stats /* * Metric type */ - if (Cfg.enable_statistics_charts) { + { if (!host->metric_type_rs) { char id_buf[1024]; char name_buf[1024]; @@ -90,7 +90,7 @@ void ml_update_dimensions_chart(ml_host_t *host, const ml_machine_learning_stats /* * Training status */ - if (Cfg.enable_statistics_charts) { + { if (!host->training_status_rs) { char id_buf[1024]; char name_buf[1024]; @@ -179,6 +179,7 @@ void ml_update_dimensions_chart(ml_host_t *host, const ml_machine_learning_stats rrdset_done(host->dimensions_rs); } + } void ml_update_host_and_detection_rate_charts(ml_host_t *host, collected_number AnomalyRate) { @@ -300,20 +301,20 @@ void ml_update_host_and_detection_rate_charts(ml_host_t *host, collected_number } } -void ml_update_training_statistics_chart(ml_training_thread_t *training_thread, const ml_training_stats_t &ts) { +void ml_update_training_statistics_chart(ml_host_t *host, const ml_training_stats_t &ts) { /* * queue stats */ { - if (!training_thread->queue_stats_rs) { + if (!host->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); + snprintfz(id_buf, 1024, "queue_stats_on_%s", localhost->machine_guid); + snprintfz(name_buf, 1024, "queue_stats_on_%s", rrdhost_hostname(localhost)); - training_thread->queue_stats_rs = rrdset_create( - localhost, + host->queue_stats_rs = rrdset_create( + host->rh, "netdata", // type id_buf, // id name_buf, // name @@ -327,35 +328,35 @@ void ml_update_training_statistics_chart(ml_training_thread_t *training_thread, localhost->rrd_update_every, // update_every RRDSET_TYPE_LINE// chart_type ); - rrdset_flag_set(training_thread->queue_stats_rs, RRDSET_FLAG_ANOMALY_DETECTION); + rrdset_flag_set(host->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); + host->queue_stats_queue_size_rd = + rrddim_add(host->queue_stats_rs, "queue_size", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + host->queue_stats_popped_items_rd = + rrddim_add(host->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); + rrddim_set_by_pointer(host->queue_stats_rs, + host->queue_stats_queue_size_rd, ts.queue_size); + rrddim_set_by_pointer(host->queue_stats_rs, + host->queue_stats_popped_items_rd, ts.num_popped_items); - rrdset_done(training_thread->queue_stats_rs); + rrdset_done(host->queue_stats_rs); } /* * training stats */ { - if (!training_thread->training_time_stats_rs) { + if (!host->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); + snprintfz(id_buf, 1024, "training_time_stats_on_%s", localhost->machine_guid); + snprintfz(name_buf, 1024, "training_time_stats_on_%s", rrdhost_hostname(localhost)); - training_thread->training_time_stats_rs = rrdset_create( - localhost, + host->training_time_stats_rs = rrdset_create( + host->rh, "netdata", // type id_buf, // id name_buf, // name @@ -369,39 +370,39 @@ void ml_update_training_statistics_chart(ml_training_thread_t *training_thread, 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); + rrdset_flag_set(host->training_time_stats_rs, RRDSET_FLAG_ANOMALY_DETECTION); + + host->training_time_stats_allotted_rd = + rrddim_add(host->training_time_stats_rs, "allotted", NULL, 1, 1000, RRD_ALGORITHM_ABSOLUTE); + host->training_time_stats_consumed_rd = + rrddim_add(host->training_time_stats_rs, "consumed", NULL, 1, 1000, RRD_ALGORITHM_ABSOLUTE); + host->training_time_stats_remaining_rd = + rrddim_add(host->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); + rrddim_set_by_pointer(host->training_time_stats_rs, + host->training_time_stats_allotted_rd, ts.allotted_ut); + rrddim_set_by_pointer(host->training_time_stats_rs, + host->training_time_stats_consumed_rd, ts.consumed_ut); + rrddim_set_by_pointer(host->training_time_stats_rs, + host->training_time_stats_remaining_rd, ts.remaining_ut); - rrdset_done(training_thread->training_time_stats_rs); + rrdset_done(host->training_time_stats_rs); } /* * training result stats */ { - if (!training_thread->training_results_rs) { + if (!host->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); + snprintfz(id_buf, 1024, "training_results_on_%s", localhost->machine_guid); + snprintfz(name_buf, 1024, "training_results_on_%s", rrdhost_hostname(localhost)); - training_thread->training_results_rs = rrdset_create( - localhost, + host->training_results_rs = rrdset_create( + host->rh, "netdata", // type id_buf, // id name_buf, // name @@ -415,61 +416,31 @@ void ml_update_training_statistics_chart(ml_training_thread_t *training_thread, 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); + rrdset_flag_set(host->training_results_rs, RRDSET_FLAG_ANOMALY_DETECTION); + + host->training_results_ok_rd = + rrddim_add(host->training_results_rs, "ok", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + host->training_results_invalid_query_time_range_rd = + rrddim_add(host->training_results_rs, "invalid-queries", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + host->training_results_not_enough_collected_values_rd = + rrddim_add(host->training_results_rs, "not-enough-values", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + host->training_results_null_acquired_dimension_rd = + rrddim_add(host->training_results_rs, "null-acquired-dimensions", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE); + host->training_results_chart_under_replication_rd = + rrddim_add(host->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); + rrddim_set_by_pointer(host->training_results_rs, + host->training_results_ok_rd, ts.training_result_ok); + rrddim_set_by_pointer(host->training_results_rs, + host->training_results_invalid_query_time_range_rd, ts.training_result_invalid_query_time_range); + rrddim_set_by_pointer(host->training_results_rs, + host->training_results_not_enough_collected_values_rd, ts.training_result_not_enough_collected_values); + rrddim_set_by_pointer(host->training_results_rs, + host->training_results_null_acquired_dimension_rd, ts.training_result_null_acquired_dimension); + rrddim_set_by_pointer(host->training_results_rs, + host->training_results_chart_under_replication_rd, ts.training_result_chart_under_replication); + + rrdset_done(host->training_results_rs); } } diff --git a/ml/ad_charts.h b/ml/ad_charts.h index 349b369a24..a973b44a51 100644 --- a/ml/ad_charts.h +++ b/ml/ad_charts.h @@ -9,6 +9,6 @@ void ml_update_dimensions_chart(ml_host_t *host, const ml_machine_learning_stats void ml_update_host_and_detection_rate_charts(ml_host_t *host, collected_number anomaly_rate); -void ml_update_training_statistics_chart(ml_training_thread_t *training_thread, const ml_training_stats_t &ts); +void ml_update_training_statistics_chart(ml_host_t *host, const ml_training_stats_t &ts); #endif /* ML_ADCHARTS_H */ diff --git a/ml/ml-dummy.c b/ml/ml-dummy.c index 8db252a841..53444e246f 100644 --- a/ml/ml-dummy.c +++ b/ml/ml-dummy.c @@ -19,8 +19,6 @@ bool ml_streaming_enabled() { void ml_init(void) {} -void ml_fini(void) {} - void ml_host_new(RRDHOST *rh) { UNUSED(rh); } @@ -88,8 +86,4 @@ bool ml_dimension_is_anomalous(RRDDIM *rd, time_t curr_time, double value, bool return false; } -void ml_update_global_statistics_charts(uint64_t models_consulted) { - UNUSED(models_consulted); -} - #endif diff --git a/ml/ml-private.h b/ml/ml-private.h index 8535c9262d..173b82e265 100644 --- a/ml/ml-private.h +++ b/ml/ml-private.h @@ -33,7 +33,6 @@ typedef struct { /* * KMeans */ - typedef struct { size_t num_clusters; size_t max_iterations; @@ -124,7 +123,6 @@ enum ml_training_result { typedef struct { // Chart/dimension we want to train - STRING *host_id; STRING *chart_id; STRING *dimension_id; @@ -170,7 +168,6 @@ typedef struct { /* * Queue */ - typedef struct { std::queue<ml_training_request_t> internal; netdata_mutex_t mutex; @@ -178,6 +175,7 @@ typedef struct { std::atomic<bool> exit; } ml_queue_t; + typedef struct { RRDDIM *rd; @@ -209,13 +207,20 @@ typedef struct { RRDHOST *rh; ml_machine_learning_stats_t mls; + ml_training_stats_t ts; calculated_number_t host_anomaly_rate; - netdata_mutex_t mutex; + std::atomic<bool> threads_running; + std::atomic<bool> threads_cancelled; + std::atomic<bool> threads_joined; ml_queue_t *training_queue; + netdata_mutex_t mutex; + + netdata_thread_t training_thread; + /* * bookkeeping for anomaly detection charts */ @@ -244,19 +249,6 @@ typedef struct { RRDSET *detector_events_rs; RRDDIM *detector_events_above_threshold_rd; RRDDIM *detector_events_new_anomaly_event_rd; -} ml_host_t; - -typedef struct { - size_t id; - netdata_thread_t nd_thread; - netdata_mutex_t nd_mutex; - - ml_queue_t *training_queue; - ml_training_stats_t training_stats; - - calculated_number_t *training_cns; - calculated_number_t *scratch_training_cns; - std::vector<DSample> training_samples; RRDSET *queue_stats_rs; RRDDIM *queue_stats_queue_size_rd; @@ -273,7 +265,7 @@ typedef struct { RRDDIM *training_results_not_enough_collected_values_rd; RRDDIM *training_results_null_acquired_dimension_rd; RRDDIM *training_results_chart_under_replication_rd; -} ml_training_thread_t; +} ml_host_t; typedef struct { bool enable_anomaly_detection; @@ -310,14 +302,6 @@ typedef struct { std::vector<uint32_t> random_nums; netdata_thread_t detection_thread; - std::atomic<bool> detection_stop; - - size_t num_training_threads; - - std::vector<ml_training_thread_t> training_threads; - std::atomic<bool> training_stop; - - bool enable_statistics_charts; } ml_config_t; void ml_config_load(ml_config_t *cfg); @@ -8,13 +8,14 @@ #include "ad_charts.h" -#define WORKER_TRAIN_QUEUE_POP 0 -#define WORKER_TRAIN_ACQUIRE_DIMENSION 1 -#define WORKER_TRAIN_QUERY 2 -#define WORKER_TRAIN_KMEANS 3 -#define WORKER_TRAIN_UPDATE_MODELS 4 -#define WORKER_TRAIN_RELEASE_DIMENSION 5 -#define WORKER_TRAIN_UPDATE_HOST 6 +typedef struct { + calculated_number_t *training_cns; + calculated_number_t *scratch_training_cns; + + std::vector<DSample> training_samples; +} ml_tls_data_t; + +static thread_local ml_tls_data_t tls_data; /* * Functions to convert enums to strings @@ -263,14 +264,7 @@ ml_queue_pop(ml_queue_t *q) { netdata_mutex_lock(&q->mutex); - ml_training_request_t req = { - NULL, // host_id - NULL, // chart id - NULL, // dimension id - 0, // current time - 0, // first entry - 0 // last entry - }; + ml_training_request_t req = { NULL, NULL, 0, 0, 0 }; while (q->internal.empty()) { pthread_cond_wait(&q->cond_var, &q->mutex); @@ -313,7 +307,7 @@ ml_queue_signal(ml_queue_t *q) */ static std::pair<calculated_number_t *, ml_training_response_t> -ml_dimension_calculated_numbers(ml_training_thread_t *training_thread, ml_dimension_t *dim, const ml_training_request_t &training_request) +ml_dimension_calculated_numbers(ml_dimension_t *dim, const ml_training_request_t &training_request) { ml_training_response_t training_response = {}; @@ -357,7 +351,7 @@ ml_dimension_calculated_numbers(ml_training_thread_t *training_thread, ml_dimens STORAGE_PRIORITY_BEST_EFFORT); size_t idx = 0; - memset(training_thread->training_cns, 0, sizeof(calculated_number_t) * max_n * (Cfg.lag_n + 1)); + memset(tls_data.training_cns, 0, sizeof(calculated_number_t) * max_n * (Cfg.lag_n + 1)); calculated_number_t last_value = std::numeric_limits<calculated_number_t>::quiet_NaN(); while (!ops->is_finished(&handle)) { @@ -374,11 +368,11 @@ ml_dimension_calculated_numbers(ml_training_thread_t *training_thread, ml_dimens training_response.db_after_t = timestamp; training_response.db_before_t = timestamp; - training_thread->training_cns[idx] = value; - last_value = training_thread->training_cns[idx]; + tls_data.training_cns[idx] = value; + last_value = tls_data.training_cns[idx]; training_response.collected_values++; } else - training_thread->training_cns[idx] = last_value; + tls_data.training_cns[idx] = last_value; idx++; } @@ -393,21 +387,20 @@ ml_dimension_calculated_numbers(ml_training_thread_t *training_thread, ml_dimens } // Find first non-NaN value. - for (idx = 0; std::isnan(training_thread->training_cns[idx]); idx++, training_response.total_values--) { } + for (idx = 0; std::isnan(tls_data.training_cns[idx]); idx++, training_response.total_values--) { } // Overwrite NaN values. if (idx != 0) - memmove(training_thread->training_cns, &training_thread->training_cns[idx], sizeof(calculated_number_t) * training_response.total_values); + memmove(tls_data.training_cns, &tls_data.training_cns[idx], sizeof(calculated_number_t) * training_response.total_values); training_response.result = TRAINING_RESULT_OK; - return { training_thread->training_cns, training_response }; + return { tls_data.training_cns, training_response }; } static enum ml_training_result -ml_dimension_train_model(ml_training_thread_t *training_thread, ml_dimension_t *dim, const ml_training_request_t &training_request) +ml_dimension_train_model(ml_dimension_t *dim, const ml_training_request_t &training_request) { - worker_is_busy(WORKER_TRAIN_QUERY); - auto P = ml_dimension_calculated_numbers(training_thread, dim, training_request); + auto P = ml_dimension_calculated_numbers(dim, training_request); ml_training_response_t training_response = P.second; if (training_response.result != TRAINING_RESULT_OK) { @@ -436,16 +429,15 @@ ml_dimension_train_model(ml_training_thread_t *training_thread, ml_dimension_t * } // compute kmeans - worker_is_busy(WORKER_TRAIN_KMEANS); { - memcpy(training_thread->scratch_training_cns, training_thread->training_cns, + memcpy(tls_data.scratch_training_cns, tls_data.training_cns, training_response.total_values * sizeof(calculated_number_t)); ml_features_t features = { Cfg.diff_n, Cfg.smooth_n, Cfg.lag_n, - training_thread->scratch_training_cns, training_response.total_values, - training_thread->training_cns, training_response.total_values, - training_thread->training_samples + tls_data.scratch_training_cns, training_response.total_values, + tls_data.training_cns, training_response.total_values, + tls_data.training_samples }; ml_features_preprocess(&features); @@ -454,7 +446,6 @@ ml_dimension_train_model(ml_training_thread_t *training_thread, ml_dimension_t * } // update kmeans models - worker_is_busy(WORKER_TRAIN_UPDATE_MODELS); { netdata_mutex_lock(&dim->mutex); @@ -506,16 +497,11 @@ ml_dimension_schedule_for_training(ml_dimension_t *dim, time_t curr_time) } if (schedule_for_training) { + ml_host_t *host = (ml_host_t *) dim->rd->rrdset->rrdhost->ml_host; ml_training_request_t req = { - string_dup(dim->rd->rrdset->rrdhost->hostname), - string_dup(dim->rd->rrdset->id), - string_dup(dim->rd->id), - curr_time, - rrddim_first_entry_s(dim->rd), - rrddim_last_entry_s(dim->rd), + string_dup(dim->rd->rrdset->id), string_dup(dim->rd->id), + curr_time, rrddim_first_entry_s(dim->rd), rrddim_last_entry_s(dim->rd), }; - - ml_host_t *host = (ml_host_t *) dim->rd->rrdset->rrdhost->ml_host; ml_queue_push(host->training_queue, req); } } @@ -691,6 +677,7 @@ ml_host_detect_once(ml_host_t *host) host->mls = {}; ml_machine_learning_stats_t mls_copy = {}; + ml_training_stats_t ts_copy = {}; { netdata_mutex_lock(&host->mutex); @@ -734,14 +721,54 @@ ml_host_detect_once(ml_host_t *host) mls_copy = host->mls; + /* + * training stats + */ + ts_copy = host->ts; + + host->ts.queue_size = 0; + host->ts.num_popped_items = 0; + + host->ts.allotted_ut = 0; + host->ts.consumed_ut = 0; + host->ts.remaining_ut = 0; + + host->ts.training_result_ok = 0; + host->ts.training_result_invalid_query_time_range = 0; + host->ts.training_result_not_enough_collected_values = 0; + host->ts.training_result_null_acquired_dimension = 0; + host->ts.training_result_chart_under_replication = 0; + netdata_mutex_unlock(&host->mutex); } + // Calc the avg values + if (ts_copy.num_popped_items) { + ts_copy.queue_size /= ts_copy.num_popped_items; + ts_copy.allotted_ut /= ts_copy.num_popped_items; + ts_copy.consumed_ut /= ts_copy.num_popped_items; + ts_copy.remaining_ut /= ts_copy.num_popped_items; + + ts_copy.training_result_ok /= ts_copy.num_popped_items; + ts_copy.training_result_invalid_query_time_range /= ts_copy.num_popped_items; + ts_copy.training_result_not_enough_collected_values /= ts_copy.num_popped_items; + ts_copy.training_result_null_acquired_dimension /= ts_copy.num_popped_items; + ts_copy.training_result_chart_under_replication /= ts_copy.num_popped_items; + } else { + ts_copy.queue_size = 0; + ts_copy.allotted_ut = 0; + ts_copy.consumed_ut = 0; + ts_copy.remaining_ut = 0; + } + worker_is_busy(WORKER_JOB_DETECTION_DIM_CHART); ml_update_dimensions_chart(host, mls_copy); worker_is_busy(WORKER_JOB_DETECTION_HOST_CHART); ml_update_host_and_detection_rate_charts(host, host->host_anomaly_rate * 10000.0); + + worker_is_busy(WORKER_JOB_DETECTION_STATS); + ml_update_training_statistics_chart(host, ts_copy); } typedef struct { @@ -750,21 +777,18 @@ typedef struct { } ml_acquired_dimension_t; |