Revert "Refactor RRD code. (#15423)" (#15723)

This reverts commit 440bd51e08fdfa2a4daa191fb68643456028a753.

dbengine was still being used for non-zero tiers
even on non-dbengine modes.

3 files changed, 56 insertions, 61 deletions

diff --git a/ml/ad_charts.cc b/ml/ad_charts.cc
index 88dbbfa66d..ca4dca1393 100644
--- a/ml/ad_charts.cc
+++ b/ml/ad_charts.cc
@@ -1,21 +1,18 @@
 // SPDX-License-Identifier: GPL-3.0-or-later
 
-#include "database/rrd.h"
 #include "ad_charts.h"
 
-#define BUF_LEN    1024
-
 void ml_update_dimensions_chart(ml_host_t *host, const ml_machine_learning_stats_t &mls) {
     /*
      * Machine learning status
     */
     if (Cfg.enable_statistics_charts) {
         if (!host->machine_learning_status_rs) {
-            char id_buf[BUF_LEN + 1];
-            char name_buf[BUF_LEN + 1];
+            char id_buf[1024];
+            char name_buf[1024];
 
-            snprintfz(id_buf, BUF_LEN, "machine_learning_status_on_%s", rrdb.localhost->machine_guid);
-            snprintfz(name_buf, BUF_LEN, "machine_learning_status_on_%s", rrdhost_hostname(rrdb.localhost));
+            snprintfz(id_buf, 1024, "machine_learning_status_on_%s", localhost->machine_guid);
+            snprintfz(name_buf, 1024, "machine_learning_status_on_%s", rrdhost_hostname(localhost));
 
             host->machine_learning_status_rs = rrdset_create(
                     host->rh,
@@ -29,7 +26,7 @@ void ml_update_dimensions_chart(ml_host_t *host, const ml_machine_learning_stats
                     NETDATA_ML_PLUGIN, // plugin
                     NETDATA_ML_MODULE_TRAINING, // module
                     NETDATA_ML_CHART_PRIO_MACHINE_LEARNING_STATUS, // priority
-                    rrdb.localhost->update_every, // update_every
+                    localhost->rrd_update_every, // update_every
                     RRDSET_TYPE_LINE // chart_type
             );
             rrdset_flag_set(host->machine_learning_status_rs , RRDSET_FLAG_ANOMALY_DETECTION);
@@ -53,11 +50,11 @@ void ml_update_dimensions_chart(ml_host_t *host, const ml_machine_learning_stats
     */
     if (Cfg.enable_statistics_charts) {
         if (!host->metric_type_rs) {
-            char id_buf[BUF_LEN + 1];
-            char name_buf[BUF_LEN + 1];
+            char id_buf[1024];
+            char name_buf[1024];
 
-            snprintfz(id_buf, BUF_LEN, "metric_types_on_%s", rrdb.localhost->machine_guid);
-            snprintfz(name_buf, BUF_LEN, "metric_types_on_%s", rrdhost_hostname(rrdb.localhost));
+            snprintfz(id_buf, 1024, "metric_types_on_%s", localhost->machine_guid);
+            snprintfz(name_buf, 1024, "metric_types_on_%s", rrdhost_hostname(localhost));
 
             host->metric_type_rs = rrdset_create(
                     host->rh,
@@ -71,7 +68,7 @@ void ml_update_dimensions_chart(ml_host_t *host, const ml_machine_learning_stats
                     NETDATA_ML_PLUGIN, // plugin
                     NETDATA_ML_MODULE_TRAINING, // module
                     NETDATA_ML_CHART_PRIO_METRIC_TYPES, // priority
-                    rrdb.localhost->update_every, // update_every
+                    localhost->rrd_update_every, // update_every
                     RRDSET_TYPE_LINE // chart_type
             );
             rrdset_flag_set(host->metric_type_rs, RRDSET_FLAG_ANOMALY_DETECTION);
@@ -95,11 +92,11 @@ void ml_update_dimensions_chart(ml_host_t *host, const ml_machine_learning_stats
     */
     if (Cfg.enable_statistics_charts) {
         if (!host->training_status_rs) {
-            char id_buf[BUF_LEN + 1];
-            char name_buf[BUF_LEN + 1];
+            char id_buf[1024];
+            char name_buf[1024];
 
-            snprintfz(id_buf, BUF_LEN, "training_status_on_%s", rrdb.localhost->machine_guid);
-            snprintfz(name_buf, BUF_LEN, "training_status_on_%s", rrdhost_hostname(rrdb.localhost));
+            snprintfz(id_buf, 1024, "training_status_on_%s", localhost->machine_guid);
+            snprintfz(name_buf, 1024, "training_status_on_%s", rrdhost_hostname(localhost));
 
             host->training_status_rs = rrdset_create(
                     host->rh,
@@ -113,7 +110,7 @@ void ml_update_dimensions_chart(ml_host_t *host, const ml_machine_learning_stats
                     NETDATA_ML_PLUGIN, // plugin
                     NETDATA_ML_MODULE_TRAINING, // module
                     NETDATA_ML_CHART_PRIO_TRAINING_STATUS, // priority
-                    rrdb.localhost->update_every, // update_every
+                    localhost->rrd_update_every, // update_every
                     RRDSET_TYPE_LINE // chart_type
             );
 
@@ -150,11 +147,11 @@ void ml_update_dimensions_chart(ml_host_t *host, const ml_machine_learning_stats
     */
     {
         if (!host->dimensions_rs) {
-            char id_buf[BUF_LEN + 1];
-            char name_buf[BUF_LEN + 1];
+            char id_buf[1024];
+            char name_buf[1024];
 
-            snprintfz(id_buf, BUF_LEN, "dimensions_on_%s", rrdb.localhost->machine_guid);
-            snprintfz(name_buf, BUF_LEN, "dimensions_on_%s", rrdhost_hostname(rrdb.localhost));
+            snprintfz(id_buf, 1024, "dimensions_on_%s", localhost->machine_guid);
+            snprintfz(name_buf, 1024, "dimensions_on_%s", rrdhost_hostname(localhost));
 
             host->dimensions_rs = rrdset_create(
                     host->rh,
@@ -168,7 +165,7 @@ void ml_update_dimensions_chart(ml_host_t *host, const ml_machine_learning_stats
                     NETDATA_ML_PLUGIN, // plugin
                     NETDATA_ML_MODULE_TRAINING, // module
                     ML_CHART_PRIO_DIMENSIONS, // priority
-                    rrdb.localhost->update_every, // update_every
+                    localhost->rrd_update_every, // update_every
                     RRDSET_TYPE_LINE // chart_type
             );
             rrdset_flag_set(host->dimensions_rs, RRDSET_FLAG_ANOMALY_DETECTION);
@@ -190,11 +187,11 @@ void ml_update_dimensions_chart(ml_host_t *host, const ml_machine_learning_stats
     // ML running
     {
         if (!host->ml_running_rs) {
-            char id_buf[BUF_LEN + 1];
-            char name_buf[BUF_LEN + 1];
+            char id_buf[1024];
+            char name_buf[1024];
 
-            snprintfz(id_buf, BUF_LEN, "ml_running_on_%s", rrdb.localhost->machine_guid);
-            snprintfz(name_buf, BUF_LEN, "ml_running_on_%s", rrdhost_hostname(rrdb.localhost));
+            snprintfz(id_buf, 1024, "ml_running_on_%s", localhost->machine_guid);
+            snprintfz(name_buf, 1024, "ml_running_on_%s", rrdhost_hostname(localhost));
 
             host->ml_running_rs = rrdset_create(
                     host->rh,
@@ -208,7 +205,7 @@ void ml_update_dimensions_chart(ml_host_t *host, const ml_machine_learning_stats
                     NETDATA_ML_PLUGIN, // plugin
                     NETDATA_ML_MODULE_DETECTION, // module
                     NETDATA_ML_CHART_RUNNING, // priority
-                    rrdb.localhost->update_every, // update_every
+                    localhost->rrd_update_every, // update_every
                     RRDSET_TYPE_LINE // chart_type
             );
             rrdset_flag_set(host->ml_running_rs, RRDSET_FLAG_ANOMALY_DETECTION);
@@ -229,11 +226,11 @@ void ml_update_host_and_detection_rate_charts(ml_host_t *host, collected_number
     */
     {
         if (!host->anomaly_rate_rs) {
-            char id_buf[BUF_LEN + 1];
-            char name_buf[BUF_LEN + 1];
+            char id_buf[1024];
+            char name_buf[1024];
 
-            snprintfz(id_buf, BUF_LEN, "anomaly_rate_on_%s", rrdb.localhost->machine_guid);
-            snprintfz(name_buf, BUF_LEN, "anomaly_rate_on_%s", rrdhost_hostname(rrdb.localhost));
+            snprintfz(id_buf, 1024, "anomaly_rate_on_%s", localhost->machine_guid);
+            snprintfz(name_buf, 1024, "anomaly_rate_on_%s", rrdhost_hostname(localhost));
 
             host->anomaly_rate_rs = rrdset_create(
                     host->rh,
@@ -247,7 +244,7 @@ void ml_update_host_and_detection_rate_charts(ml_host_t *host, collected_number
                     NETDATA_ML_PLUGIN, // plugin
                     NETDATA_ML_MODULE_DETECTION, // module
                     ML_CHART_PRIO_ANOMALY_RATE, // priority
-                    rrdb.localhost->update_every, // update_every
+                    localhost->rrd_update_every, // update_every
                     RRDSET_TYPE_LINE // chart_type
             );
             rrdset_flag_set(host->anomaly_rate_rs, RRDSET_FLAG_ANOMALY_DETECTION);
@@ -266,11 +263,11 @@ void ml_update_host_and_detection_rate_charts(ml_host_t *host, collected_number
     */
     {
         if (!host->detector_events_rs) {
-            char id_buf[BUF_LEN + 1];
-            char name_buf[BUF_LEN + 1];
+            char id_buf[1024];
+            char name_buf[1024];
 
-            snprintfz(id_buf, BUF_LEN, "anomaly_detection_on_%s", rrdb.localhost->machine_guid);
-            snprintfz(name_buf, BUF_LEN, "anomaly_detection_on_%s", rrdhost_hostname(rrdb.localhost));
+            snprintfz(id_buf, 1024, "anomaly_detection_on_%s", localhost->machine_guid);
+            snprintfz(name_buf, 1024, "anomaly_detection_on_%s", rrdhost_hostname(localhost));
 
             host->detector_events_rs = rrdset_create(
                     host->rh,
@@ -284,7 +281,7 @@ void ml_update_host_and_detection_rate_charts(ml_host_t *host, collected_number
                     NETDATA_ML_PLUGIN, // plugin
                     NETDATA_ML_MODULE_DETECTION, // module
                     ML_CHART_PRIO_DETECTOR_EVENTS, // priority
-                    rrdb.localhost->update_every, // update_every
+                    localhost->rrd_update_every, // update_every
                     RRDSET_TYPE_LINE // chart_type
             );
             rrdset_flag_set(host->detector_events_rs, RRDSET_FLAG_ANOMALY_DETECTION);
@@ -301,7 +298,7 @@ void ml_update_host_and_detection_rate_charts(ml_host_t *host, collected_number
         if (host->ml_running) {
             ONEWAYALLOC *OWA = onewayalloc_create(0);
             time_t Now = now_realtime_sec();
-            time_t Before = Now - host->rh->update_every;
+            time_t Before = Now - host->rh->rrd_update_every;
             time_t After = Before - Cfg.anomaly_detection_query_duration;
             RRDR_OPTIONS Options = static_cast<RRDR_OPTIONS>(0x00000000);
 
@@ -356,14 +353,14 @@ void ml_update_training_statistics_chart(ml_training_thread_t *training_thread,
     */
     {
         if (!training_thread->queue_stats_rs) {
-            char id_buf[BUF_LEN + 1];
-            char name_buf[BUF_LEN + 1];
+            char id_buf[1024];
+            char name_buf[1024];
 
-            snprintfz(id_buf, BUF_LEN, "training_queue_%zu_stats", training_thread->id);
-            snprintfz(name_buf, BUF_LEN, "training_queue_%zu_stats", training_thread->id);
+            snprintfz(id_buf, 1024, "training_queue_%zu_stats", training_thread->id);
+            snprintfz(name_buf, 1024, "training_queue_%zu_stats", training_thread->id);
 
             training_thread->queue_stats_rs = rrdset_create(
-                    rrdb.localhost,
+                    localhost,
                     "netdata", // type
                     id_buf, // id
                     name_buf, // name
@@ -374,7 +371,7 @@ void ml_update_training_statistics_chart(ml_training_thread_t *training_thread,
                     NETDATA_ML_PLUGIN, // plugin
                     NETDATA_ML_MODULE_TRAINING, // module
                     NETDATA_ML_CHART_PRIO_QUEUE_STATS, // priority
-                    rrdb.localhost->update_every, // update_every
+                    localhost->rrd_update_every, // update_every
                     RRDSET_TYPE_LINE// chart_type
             );
             rrdset_flag_set(training_thread->queue_stats_rs, RRDSET_FLAG_ANOMALY_DETECTION);
@@ -398,14 +395,14 @@ void ml_update_training_statistics_chart(ml_training_thread_t *training_thread,
     */
     {
         if (!training_thread->training_time_stats_rs) {
-            char id_buf[BUF_LEN + 1];
-            char name_buf[BUF_LEN + 1];
+            char id_buf[1024];
+            char name_buf[1024];
 
-            snprintfz(id_buf, BUF_LEN, "training_queue_%zu_time_stats", training_thread->id);
-            snprintfz(name_buf, BUF_LEN, "training_queue_%zu_time_stats", training_thread->id);
+            snprintfz(id_buf, 1024, "training_queue_%zu_time_stats", training_thread->id);
+            snprintfz(name_buf, 1024, "training_queue_%zu_time_stats", training_thread->id);
 
             training_thread->training_time_stats_rs = rrdset_create(
-                    rrdb.localhost,
+                    localhost,
                     "netdata", // type
                     id_buf, // id
                     name_buf, // name
@@ -416,7 +413,7 @@ void ml_update_training_statistics_chart(ml_training_thread_t *training_thread,
                     NETDATA_ML_PLUGIN, // plugin
                     NETDATA_ML_MODULE_TRAINING, // module
                     NETDATA_ML_CHART_PRIO_TRAINING_TIME_STATS, // priority
-                    rrdb.localhost->update_every, // update_every
+                    localhost->rrd_update_every, // update_every
                     RRDSET_TYPE_LINE// chart_type
             );
             rrdset_flag_set(training_thread->training_time_stats_rs, RRDSET_FLAG_ANOMALY_DETECTION);
@@ -444,14 +441,14 @@ void ml_update_training_statistics_chart(ml_training_thread_t *training_thread,
     */
     {
         if (!training_thread->training_results_rs) {
-            char id_buf[BUF_LEN + 1];
-            char name_buf[BUF_LEN + 1];
+            char id_buf[1024];
+            char name_buf[1024];
 
-            snprintfz(id_buf, BUF_LEN, "training_queue_%zu_results", training_thread->id);
-            snprintfz(name_buf, BUF_LEN, "training_queue_%zu_results", training_thread->id);
+            snprintfz(id_buf, 1024, "training_queue_%zu_results", training_thread->id);
+            snprintfz(name_buf, 1024, "training_queue_%zu_results", training_thread->id);
 
             training_thread->training_results_rs = rrdset_create(
-                    rrdb.localhost,
+                    localhost,
                     "netdata", // type
                     id_buf, // id
                     name_buf, // name
@@ -462,7 +459,7 @@ void ml_update_training_statistics_chart(ml_training_thread_t *training_thread,
                     NETDATA_ML_PLUGIN, // plugin
                     NETDATA_ML_MODULE_TRAINING, // module
                     NETDATA_ML_CHART_PRIO_TRAINING_RESULTS, // priority
-                    rrdb.localhost->update_every, // update_every
+                    localhost->rrd_update_every, // update_every
                     RRDSET_TYPE_LINE// chart_type
             );
             rrdset_flag_set(training_thread->training_results_rs, RRDSET_FLAG_ANOMALY_DETECTION);
@@ -511,7 +508,7 @@ void ml_update_global_statistics_charts(uint64_t models_consulted) {
                     , NETDATA_ML_PLUGIN // plugin
                     , NETDATA_ML_MODULE_DETECTION // module
                     , NETDATA_ML_CHART_PRIO_MACHINE_LEARNING_STATUS // priority
-                    , rrdb.localhost->update_every // update_every
+                    , localhost->rrd_update_every // update_every
                     , RRDSET_TYPE_AREA // chart_type
             );
 
diff --git a/ml/ml-dummy.c b/ml/ml-dummy.c
index efee4fdaff..2ad6cc7266 100644
--- a/ml/ml-dummy.c
+++ b/ml/ml-dummy.c
@@ -110,13 +110,11 @@ void ml_update_global_statistics_charts(uint64_t models_consulted) {
 }
 
 bool ml_host_get_host_status(RRDHOST *rh, struct ml_metrics_statistics *mlm) {
-    UNUSED(rh);
     memset(mlm, 0, sizeof(*mlm));
     return false;
 }
 
 bool ml_host_running(RRDHOST *rh) {
-    UNUSED(rh);
     return false;
 }
 
diff --git a/ml/ml.cc b/ml/ml.cc
index add5c04e77..3969674923 100644
--- a/ml/ml.cc
+++ b/ml/ml.cc
@@ -356,7 +356,7 @@ ml_dimension_calculated_numbers(ml_training_thread_t *training_thread, ml_dimens
     */
     struct storage_engine_query_handle handle;
 
-    storage_engine_query_init(dim->rd->rrdset->storage_engine_id, dim->rd->tiers[0].db_metric_handle, &handle,
+    storage_engine_query_init(dim->rd->tiers[0].backend, dim->rd->tiers[0].db_metric_handle, &handle,
               training_response.query_after_t, training_response.query_before_t,
               STORAGE_PRIORITY_BEST_EFFORT);