Reuse ML load prepared statement (#16240)
Reuse ML load prepared statement and release resources on each batch load Fix parameter to ML model load to be in seconds not usec
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@ -1539,13 +1539,20 @@ static void start_ml_model_load(uv_work_t *req __maybe_unused)
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RRDDIM *rd;
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RRDDIM_ACQUIRED *rda;
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internal_error(true, "Batch ML load loader, %zu items", ml_data->count);
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sqlite3_stmt *ml_load_stmt = NULL;
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while((PValue = JudyLFirstThenNext(ml_data->JudyL, &Index, &first))) {
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UNUSED(PValue);
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rda = (RRDDIM_ACQUIRED *) Index;
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rd = rrddim_acquired_to_rrddim(rda);
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ml_dimension_load_models(rd);
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ml_dimension_load_models(rd, &ml_load_stmt);
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rrddim_acquired_release(rda);
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}
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if (ml_load_stmt) {
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sqlite3_finalize(ml_load_stmt);
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ml_load_stmt = NULL;
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}
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worker_is_idle();
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}
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@ -100,7 +100,7 @@ bool ml_dimension_is_anomalous(RRDDIM *rd, time_t curr_time, double value, bool
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return false;
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}
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int ml_dimension_load_models(RRDDIM *rd) {
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int ml_dimension_load_models(RRDDIM *rd, sqlite3_stmt **stmp) {
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UNUSED(rd);
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return 0;
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}
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17
ml/ml.cc
17
ml/ml.cc
@ -621,7 +621,7 @@ bind_fail:
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return rc;
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}
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int ml_dimension_load_models(RRDDIM *rd) {
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int ml_dimension_load_models(RRDDIM *rd, sqlite3_stmt **active_stmt) {
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ml_dimension_t *dim = (ml_dimension_t *) rd->ml_dimension;
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if (!dim)
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return 0;
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@ -635,7 +635,7 @@ int ml_dimension_load_models(RRDDIM *rd) {
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std::vector<ml_kmeans_t> V;
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static __thread sqlite3_stmt *res = NULL;
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sqlite3_stmt *res = active_stmt ? *active_stmt : NULL;
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int rc = 0;
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int param = 0;
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@ -645,18 +645,20 @@ int ml_dimension_load_models(RRDDIM *rd) {
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}
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if (unlikely(!res)) {
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rc = prepare_statement(db, db_models_load, &res);
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rc = sqlite3_prepare_v2(db, db_models_load, -1, &res, NULL);
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if (unlikely(rc != SQLITE_OK)) {
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error_report("Failed to prepare statement to load models, rc = %d", rc);
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return 1;
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}
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if (active_stmt)
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*active_stmt = res;
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}
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rc = sqlite3_bind_blob(res, ++param, &dim->rd->metric_uuid, sizeof(dim->rd->metric_uuid), SQLITE_STATIC);
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if (unlikely(rc != SQLITE_OK))
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goto bind_fail;
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rc = sqlite3_bind_int(res, ++param, now_realtime_usec() - (Cfg.num_models_to_use * Cfg.max_train_samples));
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rc = sqlite3_bind_int64(res, ++param, now_realtime_sec() - (Cfg.num_models_to_use * Cfg.max_train_samples));
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if (unlikely(rc != SQLITE_OK))
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goto bind_fail;
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@ -702,9 +704,12 @@ int ml_dimension_load_models(RRDDIM *rd) {
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if (unlikely(rc != SQLITE_DONE))
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error_report("Failed to load models, rc = %d", rc);
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rc = sqlite3_reset(res);
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if (active_stmt)
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rc = sqlite3_reset(res);
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else
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rc = sqlite3_finalize(res);
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if (unlikely(rc != SQLITE_OK))
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error_report("Failed to reset statement when loading models, rc = %d", rc);
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error_report("Failed to %s statement when loading models, rc = %d", active_stmt ? "reset" : "finalize", rc);
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return 0;
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2
ml/ml.h
2
ml/ml.h
@ -40,7 +40,7 @@ void ml_dimension_new(RRDDIM *rd);
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void ml_dimension_delete(RRDDIM *rd);
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bool ml_dimension_is_anomalous(RRDDIM *rd, time_t curr_time, double value, bool exists);
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int ml_dimension_load_models(RRDDIM *rd);
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int ml_dimension_load_models(RRDDIM *rd, sqlite3_stmt **stmt);
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void ml_update_global_statistics_charts(uint64_t models_consulted);
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