Boost dbengine (#14832)

* configure extent cache size

* workers can now execute up to 10 jobs in a run, boosting query prep and extent reads

* fix dispatched and executing counters

* boost to the max

* increase libuv worker threads

* query prep always get more prio than extent reads; stop processing in batch when dbengine is queue is critical

* fix accounting of query prep

* inlining of time-grouping functions, to speed up queries with billions of points

* make switching based on a local const variable

* print one pending contexts loading message per iteration

* inlined store engine query API

* inlined storage engine data collection api

* inlined all storage engine query ops

* eliminate and inline data collection ops

* simplified query group-by

* more error handling

* optimized partial trimming of group-by queries

* preparative work to support multiple passes of group-by

* more preparative work to support multiple passes of group-by (accepts multiple group-by params)

* unified query timings

* unified query timings - weights endpoint

* query target is no longer a static thread variable - there is a list of cached query targets, each of which of freed every 1000 queries

* fix query memory accounting

* added summary.dimension[].pri and sorted summary.dimensions based on priority and then name

* limit max ACLK WEB response size to 30MB

* the response type should be text/plain

* more preparative work for multiple group-by passes

* create functions for generating group by keys, ids and names

* multiple group-by passes are now supported

* parse group-by options array also with an index

* implemented percentage-of-instance group by function

* family is now merged in multi-node contexts

* prevent uninitialized use

1 files changed, 5 insertions, 8 deletions

diff --git a/ml/ml.cc b/ml/ml.cc
index cf9ea379a6..b5cf6d661d 100644
--- a/ml/ml.cc
+++ b/ml/ml.cc
@@ -341,24 +341,21 @@ ml_dimension_calculated_numbers(ml_dimension_t *dim, const ml_training_request_t
     /*
      * Execute the query
     */
-    struct storage_engine_query_ops *ops = dim->rd->tiers[0].query_ops;
     struct storage_engine_query_handle handle;
 
-    ops->init(dim->rd->tiers[0].db_metric_handle,
-              &handle,
-              training_response.query_after_t,
-              training_response.query_before_t,
+    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);
 
     size_t idx = 0;
     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)) {
+    while (!storage_engine_query_is_finished(&handle)) {
         if (idx == max_n)
             break;
 
-        STORAGE_POINT sp = ops->next_metric(&handle);
+        STORAGE_POINT sp = storage_engine_query_next_metric(&handle);
 
         time_t timestamp = sp.end_time_s;
         calculated_number_t value = sp.sum / sp.count;
@@ -376,7 +373,7 @@ ml_dimension_calculated_numbers(ml_dimension_t *dim, const ml_training_request_t
 
         idx++;
     }
-    ops->finalize(&handle);
+    storage_engine_query_finalize(&handle);
 
     global_statistics_ml_query_completed(/* points_read */ idx);