Weight sp_QuickieStore regression comparator by count_executions

The regression_metric_average and current_metric_average SELECTs built
their per-unit-work averages with AVG(qsrs.avg_cpu_time),
AVG(qsrs.avg_duration), etc. — but qsrs.avg_* are themselves per-interval
averages, so AVG over them is an unweighted mean of means.

An interval with 1 execution at 1000 ms gets the same pull on the
number as an interval with 10,000 executions at 10 ms. Regression
detection then flags sparse outliers as movement, and misses real
load changes that play out across many intervals with similar avg.

Changed the per-unit-work cases (cpu, logical/physical reads, writes,
duration, memory, tempdb, rows) in both the baseline and current
SELECTs to:

    SUM(qsrs.avg_metric * qsrs.count_executions)
      / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)

Left alone:
  - 'total *' cases — already SUM(avg * count), which matches intent.
  - 'executions' — qsrs.count_executions is a count, not an average,
    so plain AVG per interval is meaningful.
  - wait branch — waits.total_query_wait_time_ms is already a per-interval
    total, so AVG across intervals is the right shape.

Verified sp_QuickieStore installs clean and runs against
PerformanceMonitor on SQL Server 2022 with
@regression_baseline_start_date/@regression_comparator = 'relative'
/@regression_direction = 'regressed' without errors.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: erikdarlingdata

sp_QuickieStore/sp_QuickieStore.sql

@@ -8873,22 +8873,29 @@ BEGIN
         @sql += N'
     SELECT
         qsq.query_hash,
-        /* All of these but count_executions are already floats. */
+        /* All of these but count_executions are already floats.
+           qsrs.avg_* columns are themselves per-interval averages, so
+           AVG(avg_*) is an unweighted mean of means. Weight by
+           count_executions to get the true cross-interval average —
+           otherwise intervals with very few executions get the same
+           pull on the number as intervals with many, and regression
+           detection skews toward sparse outlier intervals. */
         regression_metric_average =
             CONVERT
             (
                 float,
                 ' +
                 CASE @sort_order
-                     WHEN 'cpu' THEN N'AVG(qsrs.avg_cpu_time)'
-                     WHEN 'logical reads' THEN N'AVG(qsrs.avg_logical_io_reads)'
-                     WHEN 'physical reads' THEN N'AVG(qsrs.avg_physical_io_reads)'
-                     WHEN 'writes' THEN N'AVG(qsrs.avg_logical_io_writes)'
-                     WHEN 'duration' THEN N'AVG(qsrs.avg_duration)'
-                     WHEN 'memory' THEN N'AVG(qsrs.avg_query_max_used_memory)'
-                     WHEN 'tempdb' THEN CASE WHEN @new = 1 THEN N'AVG(qsrs.avg_tempdb_space_used)' ELSE N'AVG(qsrs.avg_cpu_time)' END
+                     WHEN 'cpu' THEN N'SUM(qsrs.avg_cpu_time * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)'
+                     WHEN 'logical reads' THEN N'SUM(qsrs.avg_logical_io_reads * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)'
+                     WHEN 'physical reads' THEN N'SUM(qsrs.avg_physical_io_reads * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)'
+                     WHEN 'writes' THEN N'SUM(qsrs.avg_logical_io_writes * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)'
+                     WHEN 'duration' THEN N'SUM(qsrs.avg_duration * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)'
+                     WHEN 'memory' THEN N'SUM(qsrs.avg_query_max_used_memory * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)'
+                     WHEN 'tempdb' THEN CASE WHEN @new = 1 THEN N'SUM(qsrs.avg_tempdb_space_used * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)' ELSE N'SUM(qsrs.avg_cpu_time * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)' END
+                     /* count_executions per interval is meaningful as a plain mean — it''s a count, not an average-of-averages. */
                      WHEN 'executions' THEN N'AVG(qsrs.count_executions)'
-                     WHEN 'rows' THEN N'AVG(qsrs.avg_rowcount)'
+                     WHEN 'rows' THEN N'SUM(qsrs.avg_rowcount * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)'
                      WHEN 'total cpu' THEN N'SUM(qsrs.avg_cpu_time * qsrs.count_executions)'
                      WHEN 'total logical reads' THEN N'SUM(qsrs.avg_logical_io_reads * qsrs.count_executions)'
                      WHEN 'total physical reads' THEN N'SUM(qsrs.avg_physical_io_reads * qsrs.count_executions)'
@@ -8897,7 +8904,8 @@ BEGIN
                      WHEN 'total memory' THEN N'SUM(qsrs.avg_query_max_used_memory * qsrs.count_executions)'
                      WHEN 'total tempdb' THEN CASE WHEN @new = 1 THEN N'SUM(qsrs.avg_tempdb_space_used * qsrs.count_executions)' ELSE N'SUM(qsrs.avg_cpu_time * qsrs.count_executions)' END
                      WHEN 'total rows' THEN N'SUM(qsrs.avg_rowcount * qsrs.count_executions)'
-                     ELSE CASE WHEN @sort_order_is_a_wait = 1 THEN N'AVG(waits.total_query_wait_time_ms)' ELSE N'AVG(qsrs.avg_cpu_time)' END
+                     /* Waits and the fallback path — waits are per-interval totals so AVG is correct; fallback mirrors cpu path. */
+                     ELSE CASE WHEN @sort_order_is_a_wait = 1 THEN N'AVG(waits.total_query_wait_time_ms)' ELSE N'SUM(qsrs.avg_cpu_time * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)' END
                 END
                 + N'
             )
@@ -8985,22 +8993,25 @@ BEGIN
         @sql += N'
     SELECT
         qsq.query_hash,
-        /* All of these but count_executions are already floats. */
+        /* All of these but count_executions are already floats.
+           Weighted by count_executions so the current-window average
+           matches the baseline-window computation (see baseline block
+           above) and regression percentages compare like with like. */
         current_metric_average =
             CONVERT
             (
                 float,
                 ' +
                 CASE @sort_order
-                     WHEN 'cpu' THEN N'AVG(qsrs.avg_cpu_time)'
-                     WHEN 'logical reads' THEN N'AVG(qsrs.avg_logical_io_reads)'
-                     WHEN 'physical reads' THEN N'AVG(qsrs.avg_physical_io_reads)'
-                     WHEN 'writes' THEN N'AVG(qsrs.avg_logical_io_writes)'
-                     WHEN 'duration' THEN N'AVG(qsrs.avg_duration)'
-                     WHEN 'memory' THEN N'AVG(qsrs.avg_query_max_used_memory)'
-                     WHEN 'tempdb' THEN CASE WHEN @new = 1 THEN N'AVG(qsrs.avg_tempdb_space_used)' ELSE N'AVG(qsrs.avg_cpu_time)' END
+                     WHEN 'cpu' THEN N'SUM(qsrs.avg_cpu_time * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)'
+                     WHEN 'logical reads' THEN N'SUM(qsrs.avg_logical_io_reads * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)'
+                     WHEN 'physical reads' THEN N'SUM(qsrs.avg_physical_io_reads * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)'
+                     WHEN 'writes' THEN N'SUM(qsrs.avg_logical_io_writes * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)'
+                     WHEN 'duration' THEN N'SUM(qsrs.avg_duration * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)'
+                     WHEN 'memory' THEN N'SUM(qsrs.avg_query_max_used_memory * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)'
+                     WHEN 'tempdb' THEN CASE WHEN @new = 1 THEN N'SUM(qsrs.avg_tempdb_space_used * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)' ELSE N'SUM(qsrs.avg_cpu_time * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)' END
                      WHEN 'executions' THEN N'AVG(qsrs.count_executions)'
-                     WHEN 'rows' THEN N'AVG(qsrs.avg_rowcount)'
+                     WHEN 'rows' THEN N'SUM(qsrs.avg_rowcount * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)'
                      WHEN 'total cpu' THEN N'SUM(qsrs.avg_cpu_time * qsrs.count_executions)'
                      WHEN 'total logical reads' THEN N'SUM(qsrs.avg_logical_io_reads * qsrs.count_executions)'
                      WHEN 'total physical reads' THEN N'SUM(qsrs.avg_physical_io_reads * qsrs.count_executions)'
@@ -9009,7 +9020,7 @@ BEGIN
                      WHEN 'total memory' THEN N'SUM(qsrs.avg_query_max_used_memory * qsrs.count_executions)'
                      WHEN 'total tempdb' THEN CASE WHEN @new = 1 THEN N'SUM(qsrs.avg_tempdb_space_used * qsrs.count_executions)' ELSE N'SUM(qsrs.avg_cpu_time * qsrs.count_executions)' END
                      WHEN 'total rows' THEN N'SUM(qsrs.avg_rowcount * qsrs.count_executions)'
-                     ELSE CASE WHEN @sort_order_is_a_wait = 1 THEN N'AVG(waits.total_query_wait_time_ms)' ELSE N'AVG(qsrs.avg_cpu_time)' END
+                     ELSE CASE WHEN @sort_order_is_a_wait = 1 THEN N'AVG(waits.total_query_wait_time_ms)' ELSE N'SUM(qsrs.avg_cpu_time * qsrs.count_executions) / NULLIF(SUM(CONVERT(float, qsrs.count_executions)), 0)' END
                 END
                 + N'
             )