| 1 | // SPDX-License-Identifier: GPL-3.0-or-later |
| 2 | |
| 3 | #include "ml_private.h" |
| 4 | |
| 5 | #include <array> |
| 6 | |
| 7 | #include "ad_charts.h" |
| 8 | #include "database/sqlite/vendored/sqlite3.h" |
| 9 | #include "streaming/stream-control.h" |
| 10 | |
| 11 | #define WORKER_TRAIN_QUEUE_POP 0 |
| 12 | #define WORKER_TRAIN_ACQUIRE_DIMENSION 1 |
| 13 | #define WORKER_TRAIN_QUERY 2 |
| 14 | #define WORKER_TRAIN_KMEANS 3 |
| 15 | #define WORKER_TRAIN_UPDATE_MODELS 4 |
| 16 | #define WORKER_TRAIN_RELEASE_DIMENSION 5 |
| 17 | #define WORKER_TRAIN_UPDATE_HOST 6 |
| 18 | #define WORKER_TRAIN_FLUSH_MODELS 7 |
| 19 | |
| 20 | sqlite3 *ml_db = NULL; |
| 21 | static netdata_mutex_t db_mutex; |
| 22 | |
| 23 | static void __attribute__((constructor)) init_mutex(void) { |
| 24 | netdata_mutex_init(&db_mutex); |
| 25 | } |
| 26 | |
| 27 | static void __attribute__((destructor)) destroy_mutex(void) { |
| 28 | netdata_mutex_destroy(&db_mutex); |
| 29 | } |
| 30 | |
| 31 | namespace { |
| 32 | |
| 33 | // Guard against silent truncation when time_t is narrower than sqlite3_int64 |
| 34 | // (e.g. 32-bit builds). if constexpr ensures the dead branches are elided at |
| 35 | // compile time on 64-bit platforms, avoiding constant-expression warnings. |
| 36 | static inline bool ml_sqlite_int64_fits_time_t(sqlite3_int64 value) |
| 37 | { |
| 38 | if constexpr (!std::numeric_limits<time_t>::is_signed || |
| 39 | std::numeric_limits<time_t>::digits < std::numeric_limits<sqlite3_int64>::digits) { |
| 40 | // coverity[CONSTANT_EXPRESSION_RESULT] - on 64-bit, Coverity still analyzes |
| 41 | // this dead branch; reachable only on 32-bit or unsigned time_t builds. |
| 42 | if (value < (sqlite3_int64) std::numeric_limits<time_t>::min()) |
| 43 | return false; |
| 44 | } |
| 45 | if constexpr (std::numeric_limits<time_t>::digits < std::numeric_limits<sqlite3_int64>::digits) { |
| 46 | // coverity[CONSTANT_EXPRESSION_RESULT] - dead on same-width 64-bit time_t; |
| 47 | // reachable only when time_t is narrower than sqlite3_int64, but Coverity |
| 48 | // still analyzes this discarded if constexpr branch. |
| 49 | if (value > (sqlite3_int64) std::numeric_limits<time_t>::max()) |
| 50 | return false; |
| 51 | } |
| 52 | return true; |
| 53 | } |
| 54 | |
| 55 | } |
| 56 | |
| 57 | static inline size_t ml_dimension_smoothing_window(const ml_dimension_t *dim) |
| 58 | { |
| 59 | unsigned chart_update_every = dim->rd->rrdset->update_every; |
| 60 | if (chart_update_every > nd_profile.update_every) |
| 61 | return 1; |
| 62 | |
| 63 | // max_samples_to_smooth == 0 is normalized to an effective smoothing window |
| 64 | // of 1 for feature extraction. |
| 65 | return std::max<size_t>(Cfg.max_samples_to_smooth, 1); |
| 66 | } |
| 67 | |
| 68 | typedef struct { |
| 69 | // First/last entry of the dimension in DB when generating the response |
| 70 | time_t first_entry_on_response; |
| 71 | time_t last_entry_on_response; |
| 72 | |
| 73 | // After/Before timestamps of our DB query |
| 74 | time_t query_after_t; |
| 75 | time_t query_before_t; |
| 76 | |
| 77 | // Actual after/before returned by the DB query ops |
| 78 | time_t db_after_t; |
| 79 | time_t db_before_t; |
| 80 | |
| 81 | // Number of doubles returned by the DB query |
| 82 | size_t collected_values; |
| 83 | |
| 84 | // Number of values we return to the caller |
| 85 | size_t total_values; |
| 86 | } ml_training_response_t; |
| 87 | |
| 88 | static std::pair<enum ml_worker_result, ml_training_response_t> |
| 89 | ml_dimension_calculated_numbers(ml_worker_t *worker, ml_dimension_t *dim) |
| 90 | { |
| 91 | ml_training_response_t training_response = {}; |
| 92 | |
| 93 | training_response.first_entry_on_response = rrddim_first_entry_s_of_tier(dim->rd, 0); |
| 94 | training_response.last_entry_on_response = rrddim_last_entry_s_of_tier(dim->rd, 0); |
| 95 | |
| 96 | unsigned chart_update_every = dim->rd->rrdset->update_every; |
| 97 | size_t smoothing_window = ml_dimension_smoothing_window(dim); |
| 98 | size_t min_required_samples = Cfg.diff_n + smoothing_window + Cfg.lag_n; |
| 99 | |
| 100 | auto round_up_div = [](time_t window, unsigned step) -> size_t { |
| 101 | if (window <= 0 || step == 0) |
| 102 | return 0; |
| 103 | return static_cast<size_t>((window + step - 1) / step); |
| 104 | }; |
| 105 | |
| 106 | size_t min_n = round_up_div(Cfg.min_training_window, chart_update_every); |
| 107 | size_t max_n = round_up_div(Cfg.training_window, chart_update_every); |
| 108 | |
| 109 | if (min_n < min_required_samples) |
| 110 | min_n = min_required_samples; |
| 111 | if (max_n < min_required_samples) |
| 112 | max_n = min_required_samples; |
| 113 | |
| 114 | // Figure out what our time window should be. |
| 115 | training_response.query_before_t = training_response.last_entry_on_response; |
| 116 | training_response.query_after_t = std::max( |
| 117 | training_response.query_before_t - Cfg.training_window, // Fixed time window |
| 118 | training_response.first_entry_on_response |
| 119 | ); |
| 120 | |
| 121 | if (training_response.query_after_t >= training_response.query_before_t) { |
| 122 | return { ML_WORKER_RESULT_INVALID_QUERY_TIME_RANGE, training_response }; |
| 123 | } |
| 124 | |
| 125 | if (rrdset_is_replicating(dim->rd->rrdset)) { |
| 126 | return { ML_WORKER_RESULT_CHART_UNDER_REPLICATION, training_response }; |
| 127 | } |
| 128 | |
| 129 | /* |
| 130 | * Execute the query |
| 131 | */ |
| 132 | struct storage_engine_query_handle handle; |
| 133 | |
| 134 | storage_engine_query_init(dim->rd->tiers[0].seb, dim->rd->tiers[0].smh, &handle, |
| 135 | training_response.query_after_t, training_response.query_before_t, |
| 136 | STORAGE_PRIORITY_SYNCHRONOUS); |
| 137 | |
| 138 | size_t idx = 0; |
| 139 | memset(worker->training_cns, 0, sizeof(calculated_number_t) * max_n * (Cfg.lag_n + 1)); |
| 140 | calculated_number_t last_value = std::numeric_limits<calculated_number_t>::quiet_NaN(); |
| 141 | |
| 142 | while (!storage_engine_query_is_finished(&handle)) { |
| 143 | if (idx == max_n) |
| 144 | break; |
| 145 | |
| 146 | STORAGE_POINT sp = storage_engine_query_next_metric(&handle); |
| 147 | |
| 148 | time_t timestamp = sp.end_time_s; |
| 149 | calculated_number_t value = sp.sum / sp.count; |
| 150 | |
| 151 | if (netdata_double_isnumber(value)) { |
| 152 | if (!training_response.db_after_t) |
| 153 | training_response.db_after_t = timestamp; |
| 154 | training_response.db_before_t = timestamp; |
| 155 | |
| 156 | worker->training_cns[idx] = value; |
| 157 | last_value = worker->training_cns[idx]; |
| 158 | training_response.collected_values++; |
| 159 | } else |
| 160 | worker->training_cns[idx] = last_value; |
| 161 | |
| 162 | idx++; |
| 163 | } |
| 164 | storage_engine_query_finalize(&handle); |
| 165 | |
| 166 | pulse_queries_ml_query_completed(/* points_read */ idx); |
| 167 | |
| 168 | training_response.total_values = idx; |
| 169 | if (training_response.collected_values < min_n) { |
| 170 | return { ML_WORKER_RESULT_NOT_ENOUGH_COLLECTED_VALUES, training_response }; |
| 171 | } |
| 172 | |
| 173 | // Find first non-NaN value. |
| 174 | for (idx = 0; std::isnan(worker->training_cns[idx]); idx++, training_response.total_values--) { } |
| 175 | |
| 176 | // Overwrite NaN values. |
| 177 | if (idx != 0) |
| 178 | memmove(worker->training_cns, &worker->training_cns[idx], sizeof(calculated_number_t) * training_response.total_values); |
| 179 | |
| 180 | if (training_response.total_values < min_required_samples) |
| 181 | return { ML_WORKER_RESULT_NOT_ENOUGH_COLLECTED_VALUES, training_response }; |
| 182 | |
| 183 | return { ML_WORKER_RESULT_OK, training_response }; |
| 184 | } |
| 185 | |
| 186 | const char *db_models_create_table = |
| 187 | "CREATE TABLE IF NOT EXISTS models(" |
| 188 | " dim_id BLOB, after INT, before INT," |
| 189 | " min_dist REAL, max_dist REAL," |
| 190 | " c00 REAL, c01 REAL, c02 REAL, c03 REAL, c04 REAL, c05 REAL," |
| 191 | " c10 REAL, c11 REAL, c12 REAL, c13 REAL, c14 REAL, c15 REAL," |
| 192 | " PRIMARY KEY(dim_id, after)" |
| 193 | ");"; |
| 194 | |
| 195 | const char *db_models_add_model = |
| 196 | "INSERT OR REPLACE INTO models(" |
| 197 | " dim_id, after, before," |
| 198 | " min_dist, max_dist," |
| 199 | " c00, c01, c02, c03, c04, c05," |
| 200 | " c10, c11, c12, c13, c14, c15)" |
| 201 | "VALUES(" |
| 202 | " @dim_id, @after, @before," |
| 203 | " @min_dist, @max_dist," |
| 204 | " @c00, @c01, @c02, @c03, @c04, @c05," |
| 205 | " @c10, @c11, @c12, @c13, @c14, @c15);"; |
| 206 | |
| 207 | const char *db_models_load = |
| 208 | "SELECT after, before, min_dist, max_dist, " |
| 209 | "c00, c01, c02, c03, c04, c05, " |
| 210 | "c10, c11, c12, c13, c14, c15 FROM (" |
| 211 | "SELECT after, before, min_dist, max_dist, " |
| 212 | "c00, c01, c02, c03, c04, c05, " |
| 213 | "c10, c11, c12, c13, c14, c15 FROM models " |
| 214 | "WHERE dim_id = @dim_id AND after >= @after " |
| 215 | "ORDER BY after DESC LIMIT @n" |
| 216 | ") ORDER BY after ASC;"; |
| 217 | |
| 218 | const char *db_models_delete = |
| 219 | "DELETE FROM models " |
| 220 | "WHERE dim_id = @dim_id AND before < @before;"; |
| 221 | |
| 222 | const char *db_models_prune = |
| 223 | "DELETE FROM models " |
| 224 | "WHERE after < @after LIMIT @n;"; |
| 225 | |
| 226 | static int |
| 227 | ml_dimension_add_model(const nd_uuid_t *metric_uuid, const ml_kmeans_inlined_t *inlined_km) |
| 228 | { |
| 229 | static __thread sqlite3_stmt *res = NULL; |
| 230 | int param = 0; |
| 231 | int rc = 0; |
| 232 | |
| 233 | if (unlikely(!ml_db)) { |
| 234 | nd_log_limit_static_global_var(erl, 1, 0); |
| 235 | nd_log_limit(&erl, NDLS_DAEMON, NDLP_ERR, "ML: Database has not been initialized to add ML models"); |
| 236 | return 1; |
| 237 | } |
| 238 | |
| 239 | if (unlikely(!res)) { |
| 240 | rc = prepare_statement(ml_db, db_models_add_model, &res); |
| 241 | if (unlikely(rc != SQLITE_OK)) { |
| 242 | error_report("Failed to prepare statement to store model, rc = %d", rc); |
| 243 | return 1; |
| 244 | } |
| 245 | } |
| 246 | |
| 247 | rc = sqlite3_bind_blob(res, ++param, metric_uuid, sizeof(*metric_uuid), SQLITE_STATIC); |
| 248 | if (unlikely(rc != SQLITE_OK)) |
| 249 | goto bind_fail; |
| 250 | |
| 251 | rc = sqlite3_bind_int64(res, ++param, (sqlite3_int64) inlined_km->after); |
| 252 | if (unlikely(rc != SQLITE_OK)) |
| 253 | goto bind_fail; |
| 254 | |
| 255 | rc = sqlite3_bind_int64(res, ++param, (sqlite3_int64) inlined_km->before); |
| 256 | if (unlikely(rc != SQLITE_OK)) |
| 257 | goto bind_fail; |
| 258 | |
| 259 | rc = sqlite3_bind_double(res, ++param, inlined_km->min_dist); |
| 260 | if (unlikely(rc != SQLITE_OK)) |
| 261 | goto bind_fail; |
| 262 | |
| 263 | rc = sqlite3_bind_double(res, ++param, inlined_km->max_dist); |
| 264 | if (unlikely(rc != SQLITE_OK)) |
| 265 | goto bind_fail; |
| 266 | |
| 267 | for (const DSample &ds : inlined_km->cluster_centers) { |
| 268 | if (ds.size() != 6) |
| 269 | fatal("Expected dsample with 6 dimensions, got %ld", ds.size()); |
| 270 | |
| 271 | for (long idx = 0; idx != ds.size(); idx++) { |
| 272 | calculated_number_t cn = ds(idx); |
| 273 | int rc = sqlite3_bind_double(res, ++param, cn); |
| 274 | if (unlikely(rc != SQLITE_OK)) |
| 275 | goto bind_fail; |
| 276 | } |
| 277 | } |
| 278 | |
| 279 | rc = execute_insert(res); |
| 280 | if (unlikely(rc != SQLITE_DONE)) { |
| 281 | error_report("Failed to store model, rc = %d", rc); |
| 282 | return rc; |
| 283 | } |
| 284 | |
| 285 | rc = sqlite3_reset(res); |
| 286 | if (unlikely(rc != SQLITE_OK)) { |
| 287 | error_report("Failed to reset statement when storing model, rc = %d", rc); |
| 288 | return rc; |
| 289 | } |
| 290 | |
| 291 | return 0; |
| 292 | |
| 293 | bind_fail: |
| 294 | error_report("Failed to bind parameter %d to store model, rc = %d", param, rc); |
| 295 | rc = sqlite3_reset(res); |
| 296 | if (unlikely(rc != SQLITE_OK)) |
| 297 | error_report("Failed to reset statement to store model, rc = %d", rc); |
| 298 | return rc; |
| 299 | } |
| 300 | |
| 301 | static int |
| 302 | ml_dimension_delete_models(const nd_uuid_t *metric_uuid, time_t before) |
| 303 | { |
| 304 | static __thread sqlite3_stmt *res = NULL; |
| 305 | int rc = 0; |
| 306 | int param = 0; |
| 307 | |
| 308 | if (unlikely(!ml_db)) { |
| 309 | nd_log_limit_static_global_var(erl, 1, 0); |
| 310 | nd_log_limit(&erl, NDLS_DAEMON, NDLP_ERR, "ML: Database has not been initialized to delete ML models"); |
| 311 | return 1; |
| 312 | } |
| 313 | |
| 314 | if (unlikely(!res)) { |
| 315 | rc = prepare_statement(ml_db, db_models_delete, &res); |
| 316 | if (unlikely(rc != SQLITE_OK)) { |
| 317 | error_report("Failed to prepare statement to delete models, rc = %d", rc); |
| 318 | return rc; |
| 319 | } |
| 320 | } |
| 321 | |
| 322 | rc = sqlite3_bind_blob(res, ++param, metric_uuid, sizeof(*metric_uuid), SQLITE_STATIC); |
| 323 | if (unlikely(rc != SQLITE_OK)) |
| 324 | goto bind_fail; |
| 325 | |
| 326 | rc = sqlite3_bind_int64(res, ++param, (sqlite3_int64) before); |
| 327 | if (unlikely(rc != SQLITE_OK)) |
| 328 | goto bind_fail; |
| 329 | |
| 330 | rc = execute_insert(res); |
| 331 | if (unlikely(rc != SQLITE_DONE)) { |
| 332 | error_report("Failed to delete models, rc = %d", rc); |
| 333 | return rc; |
| 334 | } |
| 335 | |
| 336 | rc = sqlite3_reset(res); |
| 337 | if (unlikely(rc != SQLITE_OK)) { |
| 338 | error_report("Failed to reset statement when deleting models, rc = %d", rc); |
| 339 | return rc; |
| 340 | } |
| 341 | |
| 342 | return 0; |
| 343 | |
| 344 | bind_fail: |
| 345 | error_report("Failed to bind parameter %d to delete models, rc = %d", param, rc); |
| 346 | rc = sqlite3_reset(res); |
| 347 | if (unlikely(rc != SQLITE_OK)) |
| 348 | error_report("Failed to reset statement to delete models, rc = %d", rc); |
| 349 | return rc; |
| 350 | } |
| 351 | |
| 352 | static int |
| 353 | ml_prune_old_models(size_t num_models_to_prune) |
| 354 | { |
| 355 | static __thread sqlite3_stmt *res = NULL; |
| 356 | int rc = 0; |
| 357 | int param = 0; |
| 358 | |
| 359 | if (unlikely(!ml_db)) { |
| 360 | nd_log_limit_static_global_var(erl, 1, 0); |
| 361 | nd_log_limit(&erl, NDLS_DAEMON, NDLP_ERR, "ML: Database has not been initialized to prune old ML models"); |
| 362 | return 1; |
| 363 | } |
| 364 | |
| 365 | if (unlikely(!res)) { |
| 366 | rc = prepare_statement(ml_db, db_models_prune, &res); |
| 367 | if (unlikely(rc != SQLITE_OK)) { |
| 368 | error_report("Failed to prepare statement to prune models, rc = %d", rc); |
| 369 | return rc; |
| 370 | } |
| 371 | } |
| 372 | |
| 373 | time_t after = now_realtime_sec() - (time_t) Cfg.delete_models_older_than; |
| 374 | |
| 375 | rc = sqlite3_bind_int64(res, ++param, (sqlite3_int64) after); |
| 376 | if (unlikely(rc != SQLITE_OK)) |
| 377 | goto bind_fail; |
| 378 | |
| 379 | rc = sqlite3_bind_int(res, ++param, num_models_to_prune); |
| 380 | if (unlikely(rc != SQLITE_OK)) |
| 381 | goto bind_fail; |
| 382 | |
| 383 | rc = execute_insert(res); |
| 384 | if (unlikely(rc != SQLITE_DONE)) { |
| 385 | error_report("Failed to prune old models, rc = %d", rc); |
| 386 | return rc; |
| 387 | } |
| 388 | |
| 389 | rc = sqlite3_reset(res); |
| 390 | if (unlikely(rc != SQLITE_OK)) { |
| 391 | error_report("Failed to reset statement when pruning old models, rc = %d", rc); |
| 392 | return rc; |
| 393 | } |
| 394 | |
| 395 | return 0; |
| 396 | |
| 397 | bind_fail: |
| 398 | error_report("Failed to bind parameter %d to prune old models, rc = %d", param, rc); |
| 399 | rc = sqlite3_reset(res); |
| 400 | if (unlikely(rc != SQLITE_OK)) |
| 401 | error_report("Failed to reset statement to prune old models, rc = %d", rc); |
| 402 | return rc; |
| 403 | } |
| 404 | |
| 405 | int ml_dimension_load_models(RRDDIM *rd, sqlite3_stmt **active_stmt) { |
| 406 | ml_dimension_t *dim = (ml_dimension_t *) rd->ml_dimension; |
| 407 | if (!dim) |
| 408 | return 0; |
| 409 | |
| 410 | spinlock_lock(&dim->slock); |
| 411 | bool is_empty = dim->km_contexts.empty(); |
| 412 | spinlock_unlock(&dim->slock); |
| 413 | |
| 414 | if (!is_empty) |
| 415 | return 0; |
| 416 | |
| 417 | std::vector<ml_kmeans_t> V; |
| 418 | |
| 419 | sqlite3_stmt *res = active_stmt ? *active_stmt : NULL; |
| 420 | int rc = 0; |
| 421 | int param = 0; |
| 422 | |
| 423 | if (unlikely(!ml_db)) { |
| 424 | nd_log_limit_static_global_var(erl, 1, 0); |
| 425 | nd_log_limit(&erl, NDLS_DAEMON, NDLP_ERR, "ML: Database has not been initialized to load ML models"); |
| 426 | return 1; |
| 427 | } |
| 428 | |
| 429 | if (unlikely(!res)) { |
| 430 | rc = sqlite3_prepare_v2(ml_db, db_models_load, -1, &res, NULL); |
| 431 | if (unlikely(rc != SQLITE_OK)) { |
| 432 | error_report("Failed to prepare statement to load models, rc = %d", rc); |
| 433 | return 1; |
| 434 | } |
| 435 | if (active_stmt) |
| 436 | *active_stmt = res; |
| 437 | } |
| 438 | |
| 439 | nd_uuid_t *rd_uuid = uuidmap_uuid_ptr(dim->rd->uuid); |
| 440 | rc = sqlite3_bind_blob(res, ++param, rd_uuid, sizeof(*rd_uuid), SQLITE_STATIC); |
| 441 | if (unlikely(rc != SQLITE_OK)) |
| 442 | goto bind_fail; |
| 443 | |
| 444 | rc = sqlite3_bind_int64(res, ++param, now_realtime_sec() - (Cfg.num_models_to_use * Cfg.train_every)); |
| 445 | if (unlikely(rc != SQLITE_OK)) |
| 446 | goto bind_fail; |
| 447 | |
| 448 | rc = sqlite3_bind_int64(res, ++param, Cfg.num_models_to_use); |
| 449 | if (unlikely(rc != SQLITE_OK)) |
| 450 | goto bind_fail; |
| 451 | |
| 452 | spinlock_lock(&dim->slock); |
| 453 | |
| 454 | dim->km_contexts.reserve(Cfg.num_models_to_use); |
| 455 | while ((rc = sqlite3_step_monitored(res)) == SQLITE_ROW) { |
| 456 | ml_kmeans_t km; |
| 457 | |
| 458 | sqlite3_int64 raw_after = sqlite3_column_int64(res, 0); |
| 459 | sqlite3_int64 raw_before = sqlite3_column_int64(res, 1); |
| 460 | // Protect against silent truncation when time_t is narrower than int64_t |
| 461 | // (e.g. 32-bit builds, corrupted DB, or far-future timestamps). |
| 462 | if (!ml_sqlite_int64_fits_time_t(raw_after) || |
| 463 | !ml_sqlite_int64_fits_time_t(raw_before)) { |
| 464 | error_report("Skipping ML model row with out-of-range timestamps: after=%" PRId64 " before=%" PRId64, |
| 465 | (int64_t) raw_after, (int64_t) raw_before); |
| 466 | continue; |
| 467 | } |
| 468 | |
| 469 | km.after = (time_t) raw_after; |
| 470 | km.before = (time_t) raw_before; |
| 471 | |
| 472 | km.min_dist = sqlite3_column_double(res, 2); |
| 473 | km.max_dist = sqlite3_column_double(res, 3); |
| 474 | |
| 475 | km.cluster_centers.resize(2); |
| 476 | |
| 477 | km.cluster_centers[0].set_size(Cfg.lag_n + 1); |
| 478 | km.cluster_centers[0](0) = sqlite3_column_double(res, 4); |
| 479 | km.cluster_centers[0](1) = sqlite3_column_double(res, 5); |
| 480 | km.cluster_centers[0](2) = sqlite3_column_double(res, 6); |
| 481 | km.cluster_centers[0](3) = sqlite3_column_double(res, 7); |
| 482 | km.cluster_centers[0](4) = sqlite3_column_double(res, 8); |
| 483 | km.cluster_centers[0](5) = sqlite3_column_double(res, 9); |
| 484 | |
| 485 | km.cluster_centers[1].set_size(Cfg.lag_n + 1); |
| 486 | km.cluster_centers[1](0) = sqlite3_column_double(res, 10); |
| 487 | km.cluster_centers[1](1) = sqlite3_column_double(res, 11); |
| 488 | km.cluster_centers[1](2) = sqlite3_column_double(res, 12); |
| 489 | km.cluster_centers[1](3) = sqlite3_column_double(res, 13); |
| 490 | km.cluster_centers[1](4) = sqlite3_column_double(res, 14); |
| 491 | km.cluster_centers[1](5) = sqlite3_column_double(res, 15); |
| 492 | |
| 493 | dim->km_contexts.emplace_back(km); |
| 494 | } |
| 495 | |
| 496 | if (!dim->km_contexts.empty()) { |
| 497 | dim->ts = TRAINING_STATUS_TRAINED; |
| 498 | } |
| 499 | |
| 500 | spinlock_unlock(&dim->slock); |
| 501 | |
| 502 | if (unlikely(rc != SQLITE_DONE)) |
| 503 | error_report("Failed to load models, rc = %d", rc); |
| 504 | |
| 505 | if (active_stmt) |
| 506 | rc = sqlite3_reset(res); |
| 507 | else |
| 508 | rc = sqlite3_finalize(res); |
| 509 | if (unlikely(rc != SQLITE_OK)) |
| 510 | error_report("Failed to %s statement when loading models, rc = %d", active_stmt ? "reset" : "finalize", rc); |
| 511 | |
| 512 | return 0; |
| 513 | |
| 514 | bind_fail: |
| 515 | error_report("Failed to bind parameter %d to load models, rc = %d", param, rc); |
| 516 | rc = sqlite3_reset(res); |
| 517 | if (unlikely(rc != SQLITE_OK)) |
| 518 | error_report("Failed to reset statement to load models, rc = %d", rc); |
| 519 | return 1; |
| 520 | } |
| 521 | |
| 522 | static void ml_dimension_serialize_kmeans(const ml_dimension_t *dim, BUFFER *wb) |
| 523 | { |
| 524 | RRDDIM *rd = dim->rd; |
| 525 | |
| 526 | buffer_json_initialize(wb, "\"", "\"", 0, true, BUFFER_JSON_OPTIONS_MINIFY); |
| 527 | buffer_json_member_add_string(wb, "version", "1"); |
| 528 | buffer_json_member_add_string(wb, "machine-guid", rd->rrdset->rrdhost->machine_guid); |
| 529 | buffer_json_member_add_string(wb, "chart", rrdset_id(rd->rrdset)); |
| 530 | buffer_json_member_add_string(wb, "dimension", rrddim_id(rd)); |
| 531 | |
| 532 | buffer_json_member_add_object(wb, "model"); |
| 533 | ml_kmeans_serialize(&dim->km_contexts.back(), wb); |
| 534 | buffer_json_object_close(wb); |
| 535 | |
| 536 | buffer_json_finalize(wb); |
| 537 | } |
| 538 | |
| 539 | bool |
| 540 | ml_dimension_deserialize_kmeans(const char *json_str) |
| 541 | { |
| 542 | if (!json_str) { |
| 543 | netdata_log_error("Failed to deserialize kmeans: json string is null"); |
| 544 | return false; |
| 545 | } |
| 546 | |
| 547 | struct json_object *root = json_tokener_parse(json_str); |
| 548 | if (!root) { |
| 549 | netdata_log_error("Failed to deserialize kmeans: json parsing failed"); |
| 550 | return false; |
| 551 | } |
| 552 | |
| 553 | // Check the version |
| 554 | { |
| 555 | struct json_object *tmp_obj; |
| 556 | if (!json_object_object_get_ex(root, "version", &tmp_obj)) { |
| 557 | netdata_log_error("Failed to deserialize kmeans: missing key 'version'"); |
| 558 | json_object_put(root); |
| 559 | return false; |
| 560 | } |
| 561 | if (!json_object_is_type(tmp_obj, json_type_string)) { |
| 562 | netdata_log_error("Failed to deserialize kmeans: failed to parse string for 'version'"); |
| 563 | json_object_put(root); |
| 564 | return false; |
| 565 | } |
| 566 | const char *version = json_object_get_string(tmp_obj); |
| 567 | |
| 568 | if (strcmp(version, "1")) { |
| 569 | netdata_log_error("Failed to deserialize kmeans: expected version 1"); |
| 570 | json_object_put(root); |
| 571 | return false; |
| 572 | } |
| 573 | } |
| 574 | |
| 575 | // Get the value of each key |
| 576 | std::array<const char *, 3> values; |
| 577 | { |
| 578 | std::array<const char *, 3> keys = { |
| 579 | "machine-guid", |
| 580 | "chart", |
| 581 | "dimension", |
| 582 | }; |
| 583 | |
| 584 | struct json_object *tmp_obj; |
| 585 | for (size_t i = 0; i != keys.size(); i++) { |
| 586 | if (!json_object_object_get_ex(root, keys[i], &tmp_obj)) { |
| 587 | netdata_log_error("Failed to deserialize kmeans: missing key '%s'", keys[i]); |
| 588 | json_object_put(root); |
| 589 | return false; |
| 590 | } |
| 591 | if (!json_object_is_type(tmp_obj, json_type_string)) { |
| 592 | netdata_log_error("Failed to deserialize kmeans: missing string value for key '%s'", keys[i]); |
| 593 | json_object_put(root); |
| 594 | return false; |
| 595 | } |
| 596 | values[i] = json_object_get_string(tmp_obj); |
| 597 | } |
| 598 | } |
| 599 | |
| 600 | DimensionLookupInfo DLI(values[0], values[1], values[2]); |
| 601 | |
| 602 | // Parse the kmeans model |
| 603 | ml_kmeans_inlined_t inlined_km; |
| 604 | { |
| 605 | struct json_object *kmeans_obj; |
| 606 | if (!json_object_object_get_ex(root, "model", &kmeans_obj)) { |
| 607 | netdata_log_error("Failed to deserialize kmeans: missing key 'model'"); |
| 608 | json_object_put(root); |
| 609 | return false; |
| 610 | } |
| 611 | if (!json_object_is_type(kmeans_obj, json_type_object)) { |
| 612 | netdata_log_error("Failed to deserialize kmeans: failed to parse object for 'model'"); |
| 613 | json_object_put(root); |
| 614 | return false; |
| 615 | } |
| 616 | |
| 617 | if (!ml_kmeans_deserialize(&inlined_km, kmeans_obj)) { |
| 618 | json_object_put(root); |
| 619 | return false; |
| 620 | } |
| 621 | } |
| 622 | |
| 623 | AcquiredDimension AcqDim(DLI); |
| 624 | if (!AcqDim.acquired()) { |
| 625 | json_object_put(root); |
| 626 | return false; |
| 627 | } |
| 628 | |
| 629 | ml_dimension_t *Dim = reinterpret_cast<ml_dimension_t *>(AcqDim.dimension()); |
| 630 | if (!Dim) { |
| 631 | pulse_ml_models_ignored(); |
| 632 | json_object_put(root); |
| 633 | return true; |
| 634 | } |
| 635 | |
| 636 | // ml_host may have been unpublished by ml_host_delete() concurrently; |
| 637 | // the acquired RRDHOST keeps RH alive but not RH->ml_host. |
| 638 | ml_host_t *host = AcqDim.host(); |
| 639 | if (!host) { |
| 640 | pulse_ml_models_ignored(); |
| 641 | json_object_put(root); |
| 642 | return true; |
| 643 | } |
| 644 | |
| 645 | ml_queue_item_t item; |
| 646 | item.type = ML_QUEUE_ITEM_TYPE_ADD_EXISTING_MODEL; |
| 647 | item.add_existing_model = { |
| 648 | DLI, inlined_km |
| 649 | }; |
| 650 | ml_queue_push(host->queue, item); |
| 651 | |
| 652 | json_object_put(root); |
| 653 | return true; |
| 654 | } |
| 655 | |
| 656 | static void ml_dimension_stream_kmeans(ml_worker_t *worker, const ml_dimension_t *dim) |
| 657 | { |
| 658 | struct sender_state *s = dim->rd->rrdset->rrdhost->sender; |
| 659 | if (!s) |
| 660 | return; |
| 661 | |
| 662 | if(!stream_sender_has_capabilities(dim->rd->rrdset->rrdhost, STREAM_CAP_ML_MODELS) || |
| 663 | !rrdset_check_upstream_exposed(dim->rd->rrdset) || |
| 664 | !rrddim_check_upstream_exposed(dim->rd)) |
| 665 | return; |
| 666 | |
| 667 | // Reuse worker's buffers instead of allocating new ones |
| 668 | BUFFER *payload = worker->stream_payload_buffer; |
| 669 | buffer_flush(payload); |
| 670 | ml_dimension_serialize_kmeans(dim, payload); |
| 671 | |
| 672 | BUFFER *wb = worker->stream_wb_buffer; |
| 673 | buffer_flush(wb); |
| 674 | |
| 675 | buffer_sprintf( |
| 676 | wb, PLUGINSD_KEYWORD_JSON " " PLUGINSD_KEYWORD_JSON_CMD_ML_MODEL "\n%s\n" PLUGINSD_KEYWORD_JSON_END "\n", |
| 677 | buffer_tostring(payload)); |
| 678 | |
| 679 | sender_commit_clean_buffer(s, wb, STREAM_TRAFFIC_TYPE_METADATA); |
| 680 | pulse_ml_models_sent(); |
| 681 | } |
| 682 | |
| 683 | bool ml_dimension_train_model_precheck(enum ml_metric_type mt, |
| 684 | bool has_received_downstream_model, |
| 685 | bool training_in_progress, |
| 686 | enum ml_worker_result *worker_res) |
| 687 | { |
| 688 | if (has_received_downstream_model) { |
| 689 | *worker_res = ML_WORKER_RESULT_DOWNSTREAM_MODEL_SUPPLIED; |
| 690 | return true; |
| 691 | } |
| 692 | |
| 693 | if (mt == METRIC_TYPE_CONSTANT) { |
| 694 | *worker_res = ML_WORKER_RESULT_OK; |
| 695 | return true; |
| 696 | } |
| 697 | |
| 698 | if (training_in_progress) { |
| 699 | *worker_res = ML_WORKER_RESULT_TRAINING_IN_PROGRESS; |
| 700 | return true; |
| 701 | } |
| 702 | |
| 703 | return false; |
| 704 | } |
| 705 | |
| 706 | bool ml_should_requeue_create_new_model(enum ml_worker_result worker_res) |
| 707 | { |
| 708 | // TRAINING_IN_PROGRESS keeps requeueing so the dim stays in the periodic |
| 709 | // retrain cycle; the worker loop is paced by Cfg.train_every, so this is |
| 710 | // not a tight CPU spin. |
| 711 | return worker_res != ML_WORKER_RESULT_NULL_ACQUIRED_DIMENSION && |
| 712 | worker_res != ML_WORKER_RESULT_DOWNSTREAM_MODEL_SUPPLIED; |
| 713 | } |
| 714 | |
| 715 | bool ml_should_publish_model_update(bool host_running, |
| 716 | uint32_t current_generation, |
| 717 | uint32_t expected_generation, |
| 718 | bool *training_in_progress) |
| 719 | { |
| 720 | if (!host_running || current_generation != expected_generation) { |
| 721 | if (training_in_progress) |
| 722 | *training_in_progress = false; |
| 723 | return false; |
| 724 | } |
| 725 | |
| 726 | return true; |
| 727 | } |
| 728 | |
| 729 | void ml_dimension_finalize_constant_state(ml_dimension_t *dim) |
| 730 | { |
| 731 | dim->mt = METRIC_TYPE_CONSTANT; |
| 732 | dim->ts = TRAINING_STATUS_TRAINED; |
| 733 | dim->suppression_anomaly_counter = 0; |
| 734 | dim->suppression_window_counter = 0; |
| 735 | } |
| 736 | |
| 737 | static bool ml_dimension_update_models(ml_worker_t *worker, ml_dimension_t *dim, uint32_t expected_generation, bool from_downstream) |
| 738 | { |
| 739 | worker_is_busy(WORKER_TRAIN_UPDATE_MODELS); |
| 740 | |
| 741 | spinlock_lock(&dim->slock); |
| 742 | |
| 743 | ml_host_t *host = (ml_host_t *) __atomic_load_n(&dim->rd->rrdset->rrdhost->ml_host, __ATOMIC_ACQUIRE); |
| 744 | if (!ml_should_publish_model_update(host && host->ml_running, |
| 745 | dim->reset_generation, |
| 746 | expected_generation, |
| 747 | &dim->training_in_progress)) { |
| 748 | spinlock_unlock(&dim->slock); |
| 749 | return false; |
| 750 | } |
| 751 | |
| 752 | // Mark the dim as downstream-supplied only after the publish-check passes |
| 753 | // and under the same slock as the install. Setting it earlier would risk |
| 754 | // suppressing local training if the install was cancelled. |
| 755 | if (from_downstream) |
| 756 | dim->has_received_downstream_model = true; |
| 757 | |
| 758 | if (dim->km_contexts.size() < Cfg.num_models_to_use) { |
| 759 | dim->km_contexts.emplace_back(dim->kmeans); |
| 760 | } else { |
| 761 | bool can_drop_middle_km = false; |
| 762 | |
| 763 | if (Cfg.num_models_to_use > 2) { |
| 764 | const ml_kmeans_inlined_t *old_km = &dim->km_contexts[dim->km_contexts.size() - 1]; |
| 765 | const ml_kmeans_inlined_t *middle_km = &dim->km_contexts[dim->km_contexts.size() - 2]; |
| 766 | const ml_kmeans_t *new_km = &dim->kmeans; |
| 767 | |
| 768 | can_drop_middle_km = (middle_km->after < old_km->before) && |
| 769 | (middle_km->before > new_km->after); |
| 770 | } |
| 771 | |
| 772 | if (can_drop_middle_km) { |
| 773 | dim->km_contexts.back() = dim->kmeans; |
| 774 | } else { |
| 775 | std::rotate(std::begin(dim->km_contexts), std::begin(dim->km_contexts) + 1, std::end(dim->km_contexts)); |
| 776 | dim->km_contexts[dim->km_contexts.size() - 1] = dim->kmeans; |
| 777 | } |
| 778 | } |
| 779 | |
| 780 | ml_dimension_finalize_constant_state(dim); |
| 781 | |
| 782 | // Add the latest model to the list of pending models to flush. |
| 783 | ml_model_info_t model_info; |
| 784 | nd_uuid_t *rd_uuid = uuidmap_uuid_ptr(dim->rd->uuid); |
| 785 | uuid_copy(model_info.metric_uuid, *rd_uuid); |
| 786 | model_info.inlined_kmeans = dim->km_contexts.back(); |
| 787 | worker->pending_model_info.push_back(model_info); |
| 788 | |
| 789 | ml_dimension_stream_kmeans(worker, dim); |
| 790 | |
| 791 | // Clear the training in progress flag |
| 792 | dim->training_in_progress = false; |
| 793 | |
| 794 | spinlock_unlock(&dim->slock); |
| 795 | return true; |
| 796 | } |
| 797 | |
| 798 | static enum ml_worker_result |
| 799 | ml_dimension_train_model(ml_worker_t *worker, ml_dimension_t *dim) |
| 800 | { |
| 801 | worker_is_busy(WORKER_TRAIN_QUERY); |
| 802 | |
| 803 | spinlock_lock(&dim->slock); |
| 804 | ml_worker_result precheck; |
| 805 | if (ml_dimension_train_model_precheck(dim->mt, |
| 806 | dim->has_received_downstream_model, |
| 807 | dim->training_in_progress, |
| 808 | &precheck)) { |
| 809 | if (precheck == ML_WORKER_RESULT_DOWNSTREAM_MODEL_SUPPLIED) |
| 810 | dim->create_new_model_queued = false; |
| 811 | spinlock_unlock(&dim->slock); |
| 812 | return precheck; |
| 813 | } |
| 814 | |
| 815 | // Mark training as in progress and snapshot the generation so that |
| 816 | // ml_dimension_update_models() can detect a stop/reset that happened |
| 817 | // while training was running. |
| 818 | dim->training_in_progress = true; |
| 819 | uint32_t generation = dim->reset_generation; |
| 820 | spinlock_unlock(&dim->slock); |
| 821 | |
| 822 | auto P = ml_dimension_calculated_numbers(worker, dim); |
| 823 | ml_worker_result worker_result = P.first; |
| 824 | ml_training_response_t training_response = P.second; |
| 825 | |
| 826 | if (worker_result != ML_WORKER_RESULT_OK) { |
| 827 | spinlock_lock(&dim->slock); |
| 828 | |
| 829 | dim->mt = METRIC_TYPE_CONSTANT; |
| 830 | dim->suppression_anomaly_counter = 0; |
| 831 | dim->suppression_window_counter = 0; |
| 832 | dim->training_in_progress = false; |
| 833 | |
| 834 | spinlock_unlock(&dim->slock); |
| 835 | |
| 836 | return worker_result; |
| 837 | } |
| 838 | |
| 839 | // compute kmeans |
| 840 | worker_is_busy(WORKER_TRAIN_KMEANS); |
| 841 | { |
| 842 | memcpy(worker->scratch_training_cns, worker->training_cns, |
| 843 | training_response.total_values * sizeof(calculated_number_t)); |
| 844 | |
| 845 | size_t smoothing_window = ml_dimension_smoothing_window(dim); |
| 846 | |
| 847 | ml_features_t features = { |
| 848 | Cfg.diff_n, smoothing_window, Cfg.lag_n, |
| 849 | worker->scratch_training_cns, training_response.total_values, |
| 850 | worker->training_cns, training_response.total_values |
| 851 | }; |
| 852 | |
| 853 | // Calculate dynamic sampling ratio based on expected output size |
| 854 | // After diff and smooth, we'll have approximately this many vectors |
| 855 | size_t expected_vectors = training_response.total_values; |
| 856 | if (Cfg.diff_n > 0) expected_vectors--; |
| 857 | if (smoothing_window > 1) expected_vectors = expected_vectors - smoothing_window + 1; |
| 858 | expected_vectors = expected_vectors - Cfg.lag_n; |
| 859 | |
| 860 | double sampling_ratio = 1.0; |
| 861 | if (expected_vectors > Cfg.max_training_vectors) { |
| 862 | sampling_ratio = (double)Cfg.max_training_vectors / expected_vectors; |
| 863 | } |
| 864 | |
| 865 | // Apply sampling during lag feature extraction |
| 866 | ml_features_preprocess(&features, worker->training_samples, sampling_ratio); |
| 867 | |
| 868 | // Preprocessing can leave fewer than 2 vectors after diff/smooth/lag/sampling. |
| 869 | // k-means cannot build 2 cluster centers from that input, so reuse the |
| 870 | // post-cycle state machine and bail out before kmeans_train. |
| 871 | if (worker->training_samples.size() < 2) { |
| 872 | spinlock_lock(&dim->slock); |
| 873 | ml_dimension_finalize_constant_state(dim); |
| 874 | dim->training_in_progress = false; |
| 875 | spinlock_unlock(&dim->slock); |
| 876 | return ML_WORKER_RESULT_NOT_ENOUGH_COLLECTED_VALUES; |
| 877 | } |
| 878 | |
| 879 | ml_kmeans_init(&dim->kmeans); |
| 880 | ml_kmeans_train(&dim->kmeans, worker->training_samples, Cfg.max_kmeans_iters, training_response.query_after_t, training_response.query_before_t); |
| 881 | } |
| 882 | |
| 883 | // update models |
| 884 | (void) ml_dimension_update_models(worker, dim, generation, /*from_downstream=*/false); |
| 885 | |
| 886 | return worker_result; |
| 887 | } |
| 888 | |
| 889 | bool |
| 890 | ml_dimension_predict(ml_dimension_t *dim, calculated_number_t value, bool exists) |
| 891 | { |
| 892 | // Nothing to do if ML is disabled for this dimension |
| 893 | if (dim->mls != MACHINE_LEARNING_STATUS_ENABLED) |
| 894 | return false; |
| 895 | |
| 896 | // Acquire lock to protect dim->cns from concurrent access by ml_host_stop() |
| 897 | if (spinlock_trylock(&dim->slock) == 0) |
| 898 | return false; |
| 899 | |
| 900 | // Don't treat values that don't exist as anomalous |
| 901 | if (!exists) { |
| 902 | dim->cns.clear(); |
| 903 | dim->cns_head = 0; |
| 904 | spinlock_unlock(&dim->slock); |
| 905 | return false; |
| 906 | } |
| 907 | |
| 908 | // Save the value and return if we don't have enough values for a sample |
| 909 | size_t smoothing_window = ml_dimension_smoothing_window(dim); |
| 910 | unsigned n = Cfg.diff_n + smoothing_window + Cfg.lag_n; |
| 911 | |
| 912 | size_t cns_size = dim->cns.size(); |
| 913 | |
| 914 | // The ring buffer modulus is derived from the current effective smoothing |
| 915 | // window. When the effective window changes, the existing history is only |
| 916 | // reusable if it is still a linear chronological prefix, which in this |
| 917 | // representation means cns_head == 0. Wrapped ring state from the old |
| 918 | // modulus must be discarded before warmup/indexing/linearization continue. |
| 919 | bool invalid_head = (cns_size > 0 && dim->cns_head >= cns_size); |
| 920 | bool size_changed_with_wrapped_state = (cns_size != n && dim->cns_head != 0); |
| 921 | bool shrunk_below_existing_history = (cns_size > n); |
| 922 | if (invalid_head || size_changed_with_wrapped_state || shrunk_below_existing_history) { |
| 923 | dim->cns.clear(); |
| 924 | dim->cns_head = 0; |
| 925 | cns_size = 0; |
| 926 | } |
| 927 | |
| 928 | if (cns_size < n) { |
| 929 | dim->cns.push_back(value); |
| 930 | spinlock_unlock(&dim->slock); |
| 931 | return false; |
| 932 | } |
| 933 | |
| 934 | // Compare incoming value against the most recent sample (newest_idx). |
| 935 | // |
| 936 | // The old std::rotate code compared against the oldest element being dropped — that |
| 937 | // was a side effect of rotate mechanics, not intentional design. |
| 938 | // |
| 939 | // Downstream effect: when same_value is false, we set dim->mt = METRIC_TYPE_VARIABLE. |
| 940 | // This controls two things: |
| 941 | // 1. ml_dimension_train_model() skips training when mt == METRIC_TYPE_CONSTANT. |
| 942 | // 2. Statistics reporting counts constant vs variable dimensions. |
| 943 | // |
| 944 | // Comparing against newest is safe (and more correct) because: |
| 945 | // - For truly constant series, all elements are equal — either comparison works. |
| 946 | // - For changing series, comparing against newest detects the transition on the |
| 947 | // first differing tick. The old oldest-comparison could miss transitions when |
| 948 | // the oldest element happened to equal the new value by coincidence. |
| 949 | // - mt is reset to METRIC_TYPE_CONSTANT after each training cycle, |
| 950 | // so a single false negative cannot cause a permanent misclassification. |
| 951 | size_t newest_idx = (dim->cns_head + n - 1) % n; |
| 952 | bool same_value = (dim->cns[newest_idx] == value); |
| 953 | dim->cns[dim->cns_head] = value; |
| 954 | dim->cns_head = (dim->cns_head + 1) % n; |
| 955 | |
| 956 | // Create the sample |
| 957 | calculated_number_t src_cns[128]; |
| 958 | calculated_number_t dst_cns[128]; |
| 959 | constexpr size_t src_cns_capacity = sizeof(src_cns) / sizeof(src_cns[0]); |
| 960 | constexpr size_t dst_cns_capacity = sizeof(dst_cns) / sizeof(dst_cns[0]); |
| 961 | fatal_assert((n <= src_cns_capacity && n <= dst_cns_capacity) && |
| 962 | "Static buffers too small to perform prediction. " |
| 963 | "This should not be possible with the default clamping of feature extraction options"); |
| 964 | |
| 965 | size_t first_chunk = n - dim->cns_head; |
| 966 | memcpy(src_cns, dim->cns.data() + dim->cns_head, first_chunk * sizeof(calculated_number_t)); |
| 967 | if (dim->cns_head) |
| 968 | memcpy(src_cns + first_chunk, dim->cns.data(), dim->cns_head * sizeof(calculated_number_t)); |
| 969 | memcpy(dst_cns, src_cns, n * sizeof(calculated_number_t)); |
| 970 | |
| 971 | ml_features_t features = { |
| 972 | Cfg.diff_n, smoothing_window, Cfg.lag_n, |
| 973 | dst_cns, n, src_cns, n |
| 974 | }; |
| 975 | ml_features_preprocess_predict(&features, dim->feature); |
| 976 | |
| 977 | // Mark the metric time as variable if we received different values |
| 978 | if (!same_value) |
| 979 | dim->mt = METRIC_TYPE_VARIABLE; |
| 980 | |
| 981 | // Ignore silenced dimensions |
| 982 | if (dim->ts == TRAINING_STATUS_SILENCED) { |
| 983 | spinlock_unlock(&dim->slock); |
| 984 | return false; |
| 985 | } |
| 986 | |
| 987 | dim->suppression_window_counter++; |
| 988 | |
| 989 | /* |
| 990 | * Use the KMeans models to check if the value is anomalous |
| 991 | */ |
| 992 | |
| 993 | size_t sum = 0; |
| 994 | size_t models_consulted = 0; |
| 995 | |
| 996 | for (const auto &km_ctx : dim->km_contexts) { |
| 997 | models_consulted++; |
| 998 | |
| 999 | calculated_number_t anomaly_score = ml_kmeans_anomaly_score(&km_ctx, dim->feature); |
| 1000 | if (std::isnan(anomaly_score)) |
| 1001 | continue; |
| 1002 | |
| 1003 | if (anomaly_score < (100 * Cfg.dimension_anomaly_score_threshold)) { |
| 1004 | spinlock_unlock(&dim->slock); |
| 1005 | pulse_ml_models_consulted(models_consulted); |
| 1006 | return false; |
| 1007 | } |
| 1008 | |
| 1009 | sum += 1; |
| 1010 | } |
| 1011 | |
| 1012 | dim->suppression_anomaly_counter += sum ? 1 : 0; |
| 1013 | |
| 1014 | if ((dim->suppression_anomaly_counter >= Cfg.suppression_threshold) && |
| 1015 | (dim->suppression_window_counter >= Cfg.suppression_window)) { |
| 1016 | dim->ts = TRAINING_STATUS_SILENCED; |
| 1017 | } |
| 1018 | |
| 1019 | spinlock_unlock(&dim->slock); |
| 1020 | |
| 1021 | pulse_ml_models_consulted(models_consulted); |
| 1022 | return sum; |
| 1023 | } |
| 1024 | |
| 1025 | /* |
| 1026 | * Chart |
| 1027 | */ |
| 1028 | |
| 1029 | static bool |
| 1030 | ml_chart_is_available_for_ml(ml_chart_t *chart) |
| 1031 | { |
| 1032 | return rrdset_is_available_for_exporting_and_alarms(chart->rs); |
| 1033 | } |
| 1034 | |
| 1035 | void |
| 1036 | ml_chart_update_dimension(ml_chart_t *chart, ml_dimension_t *dim, bool is_anomalous) |
| 1037 | { |
| 1038 | switch (dim->mls) { |
| 1039 | case MACHINE_LEARNING_STATUS_DISABLED_DUE_TO_EXCLUDED_CHART: |
| 1040 | chart->mls.num_machine_learning_status_disabled_sp++; |
| 1041 | return; |
| 1042 | case MACHINE_LEARNING_STATUS_ENABLED: { |
| 1043 | chart->mls.num_machine_learning_status_enabled++; |
| 1044 | |
| 1045 | switch (dim->mt) { |
| 1046 | case METRIC_TYPE_CONSTANT: |
| 1047 | chart->mls.num_metric_type_constant++; |
| 1048 | chart->mls.num_training_status_trained++; |
| 1049 | chart->mls.num_normal_dimensions++; |
| 1050 | return; |
| 1051 | case METRIC_TYPE_VARIABLE: |
| 1052 | chart->mls.num_metric_type_variable++; |
| 1053 | break; |
| 1054 | } |
| 1055 | |
| 1056 | switch (dim->ts) { |
| 1057 | case TRAINING_STATUS_UNTRAINED: |
| 1058 | chart->mls.num_training_status_untrained++; |
| 1059 | return; |
| 1060 | case TRAINING_STATUS_TRAINED: |
| 1061 | chart->mls.num_training_status_trained++; |
| 1062 | |
| 1063 | chart->mls.num_anomalous_dimensions += is_anomalous; |
| 1064 | chart->mls.num_normal_dimensions += !is_anomalous; |
| 1065 | return; |
| 1066 | case TRAINING_STATUS_SILENCED: |
| 1067 | chart->mls.num_training_status_silenced++; |
| 1068 | chart->mls.num_training_status_trained++; |
| 1069 | |
| 1070 | chart->mls.num_anomalous_dimensions += is_anomalous; |
| 1071 | chart->mls.num_normal_dimensions += !is_anomalous; |
| 1072 | return; |
| 1073 | } |
| 1074 | |
| 1075 | return; |
| 1076 | } |
| 1077 | } |
| 1078 | } |
| 1079 | |
| 1080 | /* |
| 1081 | * Host detection & training functions |
| 1082 | */ |
| 1083 | |
| 1084 | #define WORKER_JOB_DETECTION_COLLECT_STATS 0 |
| 1085 | #define WORKER_JOB_DETECTION_DIM_CHART 1 |
| 1086 | #define WORKER_JOB_DETECTION_HOST_CHART 2 |
| 1087 | #define WORKER_JOB_DETECTION_STATS 3 |
| 1088 | |
| 1089 | static void |
| 1090 | ml_host_detect_once(ml_host_t *host, ONEWAYALLOC *owa) |
| 1091 | { |
| 1092 | worker_is_busy(WORKER_JOB_DETECTION_COLLECT_STATS); |
| 1093 | |
| 1094 | ml_machine_learning_stats_t mls_copy = {}; |
| 1095 | ml_machine_learning_stats_t host_mls = {}; |
| 1096 | calculated_number_t host_anomaly_rate = 0.0; |
| 1097 | |
| 1098 | if (host->ml_running) { |
| 1099 | // Snapshot the stop generation before the unlocked walk. If it changes |
| 1100 | // by the time we publish, a stop ran while we were reading chart->mls |
| 1101 | // and the accumulated snapshot must be discarded. |
| 1102 | uint64_t stop_gen_before = host->ml_stop_generation.load(); |
| 1103 | |
| 1104 | /* |
| 1105 | * prediction/detection stats |
| 1106 | */ |
| 1107 | void *rsp = NULL; |
| 1108 | rrdset_foreach_read(rsp, host->rh) { |
| 1109 | RRDSET *rs = static_cast<RRDSET *>(rsp); |
| 1110 | |
| 1111 | ml_chart_t *chart = (ml_chart_t *) __atomic_load_n(&rs->ml_chart, __ATOMIC_ACQUIRE); |
| 1112 | if (!chart) |
| 1113 | continue; |
| 1114 | |
| 1115 | if (!ml_chart_is_available_for_ml(chart)) |
| 1116 | continue; |
| 1117 | |
| 1118 | ml_machine_learning_stats_t chart_mls = chart->mls; |
| 1119 | |
| 1120 | host_mls.num_machine_learning_status_enabled += chart_mls.num_machine_learning_status_enabled; |
| 1121 | host_mls.num_machine_learning_status_disabled_sp += chart_mls.num_machine_learning_status_disabled_sp; |
| 1122 | |
| 1123 | host_mls.num_metric_type_constant += chart_mls.num_metric_type_constant; |
| 1124 | host_mls.num_metric_type_variable += chart_mls.num_metric_type_variable; |
| 1125 | |
| 1126 | host_mls.num_training_status_untrained += chart_mls.num_training_status_untrained; |
| 1127 | host_mls.num_training_status_pending_without_model += chart_mls.num_training_status_pending_without_model; |
| 1128 | host_mls.num_training_status_trained += chart_mls.num_training_status_trained; |
| 1129 | host_mls.num_training_status_pending_with_model += chart_mls.num_training_status_pending_with_model; |
| 1130 | host_mls.num_training_status_silenced += chart_mls.num_training_status_silenced; |
| 1131 | |
| 1132 | host_mls.num_anomalous_dimensions += chart_mls.num_anomalous_dimensions; |
| 1133 | host_mls.num_normal_dimensions += chart_mls.num_normal_dimensions; |
| 1134 | |
| 1135 | if (spinlock_trylock(&host->context_anomaly_rate_spinlock)) |
| 1136 | { |
| 1137 | STRING *key = rs->context; |
| 1138 | auto &um = host->context_anomaly_rate; |
| 1139 | auto it = um.find(key); |
| 1140 | if (it == um.end()) { |
| 1141 | STRING *owned_key = string_dup(key); |
| 1142 | auto insert_result = um.emplace(owned_key, ml_context_anomaly_rate_t { |
| 1143 | .rd = NULL, |
| 1144 | .normal_dimensions = 0, |
| 1145 | .anomalous_dimensions = 0 |
| 1146 | }); |
| 1147 | if (!insert_result.second) |
| 1148 | string_freez(owned_key); |
| 1149 | it = insert_result.first; |
| 1150 | } |
| 1151 | |
| 1152 | it->second.anomalous_dimensions += chart_mls.num_anomalous_dimensions; |
| 1153 | it->second.normal_dimensions += chart_mls.num_normal_dimensions; |
| 1154 | spinlock_unlock(&host->context_anomaly_rate_spinlock); |
| 1155 | } |
| 1156 | } |
| 1157 | rrdset_foreach_done(rsp); |
| 1158 | |
| 1159 | size_t num_active_dimensions = host_mls.num_anomalous_dimensions + host_mls.num_normal_dimensions; |
| 1160 | if (num_active_dimensions) |
| 1161 | host_anomaly_rate = static_cast<double>(host_mls.num_anomalous_dimensions) / num_active_dimensions; |
| 1162 | |
| 1163 | // Publish the final host snapshot after chart traversal so chart |
| 1164 | // deletion cannot block other host->mutex users for the full walk. |
| 1165 | // Discard the snapshot if either (a) ml_running is now false, or |
| 1166 | // (b) the stop generation changed since the walk started — the |
| 1167 | // latter catches a stop+start that completed during the walk and |
| 1168 | // would otherwise pass the boolean check. In either case our |
| 1169 | // unlocked chart->mls reads may have raced ml_host_stop, so zero |
| 1170 | // the snapshot and the per-context counts. The chart updates below |
| 1171 | // run unconditionally: ml_update_dimensions_chart reads |
| 1172 | // host->ml_running directly (so the ml_running chart records the |
| 1173 | // stop), and the chart-update path resets and republishes the rest. |
| 1174 | netdata_mutex_lock(&host->mutex); |
| 1175 | uint64_t stop_gen_after = host->ml_stop_generation.load(); |
| 1176 | if (!host->ml_running || stop_gen_before != stop_gen_after) { |
| 1177 | host_mls = {}; |
| 1178 | host_anomaly_rate = 0.0; |
| 1179 | |
| 1180 | spinlock_lock(&host->context_anomaly_rate_spinlock); |
| 1181 | for (auto &p : host->context_anomaly_rate) { |
| 1182 | p.second.anomalous_dimensions = 0; |
| 1183 | p.second.normal_dimensions = 0; |
| 1184 | } |
| 1185 | spinlock_unlock(&host->context_anomaly_rate_spinlock); |
| 1186 | } |
| 1187 | host->mls = host_mls; |
| 1188 | host->host_anomaly_rate = host_anomaly_rate; |
| 1189 | mls_copy = host_mls; |
| 1190 | netdata_mutex_unlock(&host->mutex); |
| 1191 | |
| 1192 | worker_is_busy(WORKER_JOB_DETECTION_DIM_CHART); |
| 1193 | ml_update_dimensions_chart(host, mls_copy); |
| 1194 | |
| 1195 | worker_is_busy(WORKER_JOB_DETECTION_HOST_CHART); |
| 1196 | ml_update_host_and_detection_rate_charts(host, host_anomaly_rate * 10000.0, owa); |
| 1197 | } else { |
| 1198 | host->host_anomaly_rate = 0.0; |
| 1199 | } |
| 1200 | } |
| 1201 | |
| 1202 | void ml_detect_main(void *arg) |
| 1203 | { |
| 1204 | UNUSED(arg); |
| 1205 | |
| 1206 | worker_register("MLDETECT"); |
| 1207 | worker_register_job_name(WORKER_JOB_DETECTION_COLLECT_STATS, "collect stats"); |
| 1208 | worker_register_job_name(WORKER_JOB_DETECTION_DIM_CHART, "dim chart"); |
| 1209 | worker_register_job_name(WORKER_JOB_DETECTION_HOST_CHART, "host chart"); |
| 1210 | worker_register_job_name(WORKER_JOB_DETECTION_STATS, "training stats"); |
| 1211 | |
| 1212 | heartbeat_t hb; |
| 1213 | heartbeat_init(&hb, USEC_PER_SEC); |
| 1214 | |
| 1215 | // Single onewayalloc arena reused across every host and loop iteration |
| 1216 | // for the whole detect thread lifetime — one mmap/munmap pair instead |
| 1217 | // of one per host per second. The arena is reset between hosts inside |
| 1218 | // ml_update_host_and_detection_rate_charts, so peak memory stays bounded |
| 1219 | // by a single host's anomaly-rate query scratch. |
| 1220 | ONEWAYALLOC *detect_owa = onewayalloc_create(0); |
| 1221 | |
| 1222 | while (!Cfg.detection_stop && service_running(SERVICE_COLLECTORS)) { |
| 1223 | worker_is_idle(); |
| 1224 | heartbeat_next(&hb); |
| 1225 | |
| 1226 | RRDHOST *rh; |
| 1227 | rrd_rdlock(); |
| 1228 | rrdhost_foreach_read(rh) { |
| 1229 | ml_host_t *host = (ml_host_t *) __atomic_load_n(&rh->ml_host, __ATOMIC_ACQUIRE); |
| 1230 | if (!host) |
| 1231 | continue; |
| 1232 | |
| 1233 | if (!service_running(SERVICE_COLLECTORS)) |
| 1234 | break; |
| 1235 | |
| 1236 | ml_host_detect_once(host, detect_owa); |
| 1237 | } |
| 1238 | rrd_rdunlock(); |
| 1239 | |
| 1240 | if (Cfg.enable_statistics_charts) { |
| 1241 | // collect and update training thread stats |
| 1242 | for (size_t idx = 0; idx != Cfg.num_worker_threads; idx++) { |
| 1243 | ml_worker_t *worker = &Cfg.workers[idx]; |
| 1244 | |
| 1245 | netdata_mutex_lock(&worker->nd_mutex); |
| 1246 | ml_queue_stats_t queue_stats = worker->queue_stats; |
| 1247 | netdata_mutex_unlock(&worker->nd_mutex); |
| 1248 | |
| 1249 | ml_update_training_statistics_chart(worker, queue_stats); |
| 1250 | } |
| 1251 | } |
| 1252 | } |
| 1253 | |
| 1254 | onewayalloc_destroy(detect_owa); |
| 1255 | |
| 1256 | Cfg.training_stop = true; |
| 1257 | finalize_self_prepared_sql_statements(); |
| 1258 | } |
| 1259 | |
| 1260 | static void ml_flush_pending_models(ml_worker_t *worker) { |
| 1261 | static time_t next_vacuum_run = 0; |
| 1262 | int op_no = 1; |
| 1263 | |
| 1264 | // begin transaction |
| 1265 | int rc = db_execute(ml_db, "BEGIN TRANSACTION;", NULL); |
| 1266 | |
| 1267 | // add/delete models |
| 1268 | if (!rc) { |
| 1269 | op_no++; |
| 1270 | |
| 1271 | for (const auto &pending_model: worker->pending_model_info) { |
| 1272 | if (!rc) |
| 1273 | rc = ml_dimension_add_model(&pending_model.metric_uuid, &pending_model.inlined_kmeans); |
| 1274 | |
| 1275 | if (!rc) |
| 1276 | rc = ml_dimension_delete_models(&pending_model.metric_uuid, pending_model.inlined_kmeans.before - (Cfg.num_models_to_use * Cfg.train_every)); |
| 1277 | } |
| 1278 | } |
| 1279 | |
| 1280 | // prune old models |
| 1281 | if (!rc) { |
| 1282 | if ((worker->num_db_transactions % 64) == 0) { |
| 1283 | rc = ml_prune_old_models(worker->num_models_to_prune); |
| 1284 | if (!rc) |
| 1285 | worker->num_models_to_prune = 0; |
| 1286 | } |
| 1287 | } |
| 1288 | |
| 1289 | // commit transaction |
| 1290 | if (!rc) { |
| 1291 | op_no++; |
| 1292 | rc = db_execute(ml_db, "COMMIT TRANSACTION;", NULL); |
| 1293 | } |
| 1294 | |
| 1295 | // rollback transaction on failure |
| 1296 | if (rc) { |
| 1297 | netdata_log_error("Trying to rollback ML transaction because it failed with rc=%d, op_no=%d", rc, op_no); |
| 1298 | op_no++; |
| 1299 | rc = db_execute(ml_db, "ROLLBACK;", NULL); |
| 1300 | if (rc) |
| 1301 | netdata_log_error("ML transaction rollback failed with rc=%d", rc); |
| 1302 | } |
| 1303 | |
| 1304 | if (!rc) { |
| 1305 | worker->num_db_transactions++; |
| 1306 | worker->num_models_to_prune += worker->pending_model_info.size(); |
| 1307 | } |
| 1308 | |
| 1309 | vacuum_database(ml_db, "ML", 0, 0, &next_vacuum_run); |
| 1310 | |
| 1311 | worker->pending_model_info.clear(); |
| 1312 | } |
| 1313 | |
| 1314 | static enum ml_worker_result ml_worker_create_new_model(ml_worker_t *worker, ml_request_create_new_model_t req) { |
| 1315 | AcquiredDimension AcqDim(req.DLI); |
| 1316 | |
| 1317 | if (!AcqDim.acquired()) { |
| 1318 | return ML_WORKER_RESULT_NULL_ACQUIRED_DIMENSION; |
| 1319 | } |
| 1320 | |
| 1321 | ml_dimension_t *Dim = reinterpret_cast<ml_dimension_t *>(AcqDim.dimension()); |
| 1322 | return ml_dimension_train_model(worker, Dim); |
| 1323 | } |
| 1324 | |
| 1325 | static enum ml_worker_result ml_worker_add_existing_model(ml_worker_t *worker, ml_request_add_existing_model_t req) { |
| 1326 | AcquiredDimension AcqDim(req.DLI); |
| 1327 | |
| 1328 | if (!AcqDim.acquired()) { |
| 1329 | return ML_WORKER_RESULT_NULL_ACQUIRED_DIMENSION; |
| 1330 | } |
| 1331 | |
| 1332 | ml_dimension_t *Dim = reinterpret_cast<ml_dimension_t *>(AcqDim.dimension()); |
| 1333 | if (!Dim) { |
| 1334 | pulse_ml_models_ignored(); |
| 1335 | return ML_WORKER_RESULT_OK; |
| 1336 | } |
| 1337 | |
| 1338 | ml_host_t *host = (ml_host_t *) __atomic_load_n(&Dim->rd->rrdset->rrdhost->ml_host, __ATOMIC_ACQUIRE); |
| 1339 | if (!host || !host->ml_running) { |
| 1340 | pulse_ml_models_ignored(); |
| 1341 | return ML_WORKER_RESULT_OK; |
| 1342 | } |
| 1343 | |
| 1344 | spinlock_lock(&Dim->slock); |
| 1345 | |
| 1346 | // Loop detection: skip if we already have this exact model. |
| 1347 | // The (after, before) pair uniquely identifies a model per dimension and is |
| 1348 | // preserved across hops, so a model that loops back is detected as a duplicate. |
| 1349 | for (const auto &km : Dim->km_contexts) { |
| 1350 | if (km.after == req.inlined_km.after && km.before == req.inlined_km.before) { |
| 1351 | spinlock_unlock(&Dim->slock); |
| 1352 | pulse_ml_models_ignored(); |
| 1353 | return ML_WORKER_RESULT_OK; |
| 1354 | } |
| 1355 | } |
| 1356 | |
| 1357 | // Reject models that are not newer than the newest accepted model. This |
| 1358 | // prevents an older model from being re-accepted after it has been evicted |
| 1359 | // from km_contexts and later loops back from downstream. |
| 1360 | if (!Dim->km_contexts.empty()) { |
| 1361 | const auto &latest_km = Dim->km_contexts.back(); |
| 1362 | if (req.inlined_km.before <= latest_km.before) { |
| 1363 | spinlock_unlock(&Dim->slock); |
| 1364 | pulse_ml_models_ignored(); |
| 1365 | return ML_WORKER_RESULT_OK; |
| 1366 | } |
| 1367 | } |
| 1368 | |
| 1369 | // Skip if training is in progress to avoid race condition. |
| 1370 | if (Dim->training_in_progress) { |
| 1371 | spinlock_unlock(&Dim->slock); |
| 1372 | pulse_ml_models_ignored(); |
| 1373 | return ML_WORKER_RESULT_OK; |
| 1374 | } |
| 1375 | |
| 1376 | // Stage the incoming kmeans into the dim's working buffer; the actual |
| 1377 | // install into km_contexts and the has_received_downstream_model flag-set |
| 1378 | // happen inside ml_dimension_update_models() under the same slock as the |
| 1379 | // publish-check, so a concurrent ml_host_stop() either commits both or |
| 1380 | // cancels both. |
| 1381 | Dim->kmeans = req.inlined_km; |
| 1382 | uint32_t generation = Dim->reset_generation; |
| 1383 | spinlock_unlock(&Dim->slock); |
| 1384 | if (ml_dimension_update_models(worker, Dim, generation, /*from_downstream=*/true)) |
| 1385 | pulse_ml_models_received(); |
| 1386 | |
| 1387 | return ML_WORKER_RESULT_OK; |
| 1388 | } |
| 1389 | |
| 1390 | void ml_train_main(void *arg) { |
| 1391 | ml_worker_t *worker = (ml_worker_t *) arg; |
| 1392 | |
| 1393 | char worker_name[1024]; |
| 1394 | snprintfz(worker_name, 1024, "ml_worker_%zu", worker->id); |
| 1395 | worker_register("MLTRAIN"); |
| 1396 | |
| 1397 | worker_register_job_name(WORKER_TRAIN_QUEUE_POP, "pop queue"); |
| 1398 | worker_register_job_name(WORKER_TRAIN_ACQUIRE_DIMENSION, "acquire"); |
| 1399 | worker_register_job_name(WORKER_TRAIN_QUERY, "query"); |
| 1400 | worker_register_job_name(WORKER_TRAIN_KMEANS, "kmeans"); |
| 1401 | worker_register_job_name(WORKER_TRAIN_UPDATE_MODELS, "update models"); |
| 1402 | worker_register_job_name(WORKER_TRAIN_RELEASE_DIMENSION, "release"); |
| 1403 | worker_register_job_name(WORKER_TRAIN_UPDATE_HOST, "update host"); |
| 1404 | worker_register_job_name(WORKER_TRAIN_FLUSH_MODELS, "flush models"); |
| 1405 | |
| 1406 | while (!Cfg.training_stop) { |
| 1407 | if(!stream_control_ml_should_be_running()) { |
| 1408 | worker_is_idle(); |
| 1409 | stream_control_throttle(); |
| 1410 | continue; |
| 1411 | } |
| 1412 | |
| 1413 | worker_is_busy(WORKER_TRAIN_QUEUE_POP); |
| 1414 | |
| 1415 | ml_queue_stats_t loop_stats{}; |
| 1416 | |
| 1417 | ml_queue_item_t item = ml_queue_pop(worker->queue); |
| 1418 | if (item.type == ML_QUEUE_ITEM_STOP_REQUEST) { |
| 1419 | break; |
| 1420 | } |
| 1421 | |
| 1422 | ml_queue_size_t queue_size = ml_queue_size(worker->queue); |
| 1423 | |
| 1424 | usec_t allotted_ut = (Cfg.train_every * USEC_PER_SEC) / (queue_size.create_new_model + 1); |
| 1425 | if (allotted_ut > USEC_PER_SEC) |
| 1426 | allotted_ut = USEC_PER_SEC; |
| 1427 | |
| 1428 | usec_t start_ut = now_monotonic_usec(); |
| 1429 | |
| 1430 | enum ml_worker_result worker_res; |
| 1431 | |
| 1432 | switch (item.type) { |
| 1433 | case ML_QUEUE_ITEM_TYPE_CREATE_NEW_MODEL: { |
| 1434 | worker_res = ml_worker_create_new_model(worker, item.create_new_model); |
| 1435 | if (ml_should_requeue_create_new_model(worker_res)) { |
| 1436 | ml_queue_push(worker->queue, item); |
| 1437 | } |
| 1438 | break; |
| 1439 | } |
| 1440 | case ML_QUEUE_ITEM_TYPE_ADD_EXISTING_MODEL: { |
| 1441 | worker_res = ml_worker_add_existing_model(worker, item.add_existing_model); |
| 1442 | break; |
| 1443 | } |
| 1444 | default: { |
| 1445 | fatal("Unknown queue item type"); |
| 1446 | } |
| 1447 | } |
| 1448 | |
| 1449 | usec_t consumed_ut = now_monotonic_usec() - start_ut; |
| 1450 | |
| 1451 | usec_t remaining_ut = 0; |
| 1452 | if (consumed_ut < allotted_ut) |
| 1453 | remaining_ut = allotted_ut - consumed_ut; |
| 1454 | |
| 1455 | if (Cfg.enable_statistics_charts) { |
| 1456 | worker_is_busy(WORKER_TRAIN_UPDATE_HOST); |
| 1457 | |
| 1458 | ml_queue_stats_t queue_stats = ml_queue_stats(worker->queue); |
| 1459 | |
| 1460 | loop_stats.total_add_existing_model_requests_pushed = queue_stats.total_add_existing_model_requests_pushed; |
| 1461 | loop_stats.total_add_existing_model_requests_popped = queue_stats.total_add_existing_model_requests_popped; |
| 1462 | loop_stats.total_create_new_model_requests_pushed = queue_stats.total_create_new_model_requests_pushed; |
| 1463 | loop_stats.total_create_new_model_requests_popped = queue_stats.total_create_new_model_requests_popped; |
| 1464 | |
| 1465 | loop_stats.allotted_ut = allotted_ut; |
| 1466 | loop_stats.consumed_ut = consumed_ut; |
| 1467 | loop_stats.remaining_ut = remaining_ut; |
| 1468 | |
| 1469 | switch (worker_res) { |
| 1470 | case ML_WORKER_RESULT_OK: |
| 1471 | loop_stats.item_result_ok = 1; |
| 1472 | break; |
| 1473 | case ML_WORKER_RESULT_INVALID_QUERY_TIME_RANGE: |
| 1474 | loop_stats.item_result_invalid_query_time_range = 1; |
| 1475 | break; |
| 1476 | case ML_WORKER_RESULT_NOT_ENOUGH_COLLECTED_VALUES: |
| 1477 | loop_stats.item_result_not_enough_collected_values = 1; |
| 1478 | break; |
| 1479 | case ML_WORKER_RESULT_NULL_ACQUIRED_DIMENSION: |
| 1480 | loop_stats.item_result_null_acquired_dimension = 1; |
| 1481 | break; |
| 1482 | case ML_WORKER_RESULT_CHART_UNDER_REPLICATION: |
| 1483 | loop_stats.item_result_chart_under_replication = 1; |
| 1484 | break; |
| 1485 | case ML_WORKER_RESULT_DOWNSTREAM_MODEL_SUPPLIED: |
| 1486 | loop_stats.item_result_ok = 1; |
| 1487 | break; |
| 1488 | case ML_WORKER_RESULT_TRAINING_IN_PROGRESS: |
| 1489 | loop_stats.item_result_ok = 1; |
| 1490 | break; |
| 1491 | } |
| 1492 | |
| 1493 | netdata_mutex_lock(&worker->nd_mutex); |
| 1494 | |
| 1495 | worker->queue_stats.total_add_existing_model_requests_pushed = loop_stats.total_add_existing_model_requests_pushed; |
| 1496 | worker->queue_stats.total_add_existing_model_requests_popped = loop_stats.total_add_existing_model_requests_popped; |
| 1497 | |
| 1498 | worker->queue_stats.total_create_new_model_requests_pushed = loop_stats.total_create_new_model_requests_pushed; |
| 1499 | worker->queue_stats.total_create_new_model_requests_popped = loop_stats.total_create_new_model_requests_popped; |
| 1500 | |
| 1501 | worker->queue_stats.allotted_ut += loop_stats.allotted_ut; |
| 1502 | worker->queue_stats.consumed_ut += loop_stats.consumed_ut; |
| 1503 | worker->queue_stats.remaining_ut += loop_stats.remaining_ut; |
| 1504 | |
| 1505 | worker->queue_stats.item_result_ok += loop_stats.item_result_ok; |
| 1506 | worker->queue_stats.item_result_invalid_query_time_range += loop_stats.item_result_invalid_query_time_range; |
| 1507 | worker->queue_stats.item_result_not_enough_collected_values += loop_stats.item_result_not_enough_collected_values; |
| 1508 | worker->queue_stats.item_result_null_acquired_dimension += loop_stats.item_result_null_acquired_dimension; |
| 1509 | worker->queue_stats.item_result_chart_under_replication += loop_stats.item_result_chart_under_replication; |
| 1510 | |
| 1511 | netdata_mutex_unlock(&worker->nd_mutex); |
| 1512 | } |
| 1513 | |
| 1514 | bool should_sleep = true; |
| 1515 | |
| 1516 | if (worker->pending_model_info.size() >= Cfg.flush_models_batch_size) { |
| 1517 | worker_is_busy(WORKER_TRAIN_FLUSH_MODELS); |
| 1518 | netdata_mutex_lock(&db_mutex); |
| 1519 | ml_flush_pending_models(worker); |
| 1520 | netdata_mutex_unlock(&db_mutex); |
| 1521 | should_sleep = false; |
| 1522 | } |
| 1523 | |
| 1524 | if (item.type == ML_QUEUE_ITEM_TYPE_ADD_EXISTING_MODEL) { |
| 1525 | should_sleep = false; |
| 1526 | } |
| 1527 | |
| 1528 | if (!should_sleep) |
| 1529 | continue; |
| 1530 | |
| 1531 | worker_is_idle(); |
| 1532 | std::this_thread::sleep_for(std::chrono::microseconds{remaining_ut}); |
| 1533 | } |
| 1534 | finalize_self_prepared_sql_statements(); |
| 1535 | } |