sql: fix label for routine started metrics

During 25.4 in ef4acb4 we merged
a change to add counters for stmts started and executed in routines.
However, for both types we used `sql.count` label which I think is
a mistake - we should use `sql.started.count` for "started" and
`sql.count` for "executed" (similar to what we do for stmts outside of
the routines).

Release note: None

Author: yuzefovich

docs/generated/metrics/metrics.yaml

@@ -664,7 +664,7 @@ layers:
       derivative: NON_NEGATIVE_DERIVATIVE
     - name: sql.routine.delete.started.count
       exported_name: sql_routine_delete_started_count
-      labeled_name: 'sql.count{query_type: routine-started-delete}'
+      labeled_name: 'sql.started.count{query_type: routine-started-delete}'
       description: Number of SQL DELETE statements started within routine invocation
       y_axis_label: SQL Statements
       type: COUNTER
@@ -674,7 +674,7 @@ layers:
       how_to_use: This high-level metric reflects workload volume. Monitor this metric to identify abnormal application behavior or patterns over time. If abnormal patterns emerge, apply the metric's time range to the SQL Activity pages to investigate interesting outliers or patterns. For example, on the Transactions page and the Statements page, sort on the Execution Count column. To find problematic sessions, on the Sessions page, sort on the Transaction Count column. Find the sessions with high transaction counts and trace back to a user or application.
     - name: sql.routine.delete.started.count.internal
       exported_name: sql_routine_delete_started_count_internal
-      labeled_name: 'sql.count{query_type: routine-started-delete, query_internal: true}'
+      labeled_name: 'sql.started.count{query_type: routine-started-delete, query_internal: true}'
       description: Number of SQL DELETE statements started within routine invocation (internal queries)
       y_axis_label: SQL Internal Statements
       type: COUNTER
@@ -703,7 +703,7 @@ layers:
       derivative: NON_NEGATIVE_DERIVATIVE
     - name: sql.routine.insert.started.count
       exported_name: sql_routine_insert_started_count
-      labeled_name: 'sql.count{query_type: routine-started-insert}'
+      labeled_name: 'sql.started.count{query_type: routine-started-insert}'
       description: Number of SQL INSERT statements started within routine invocation
       y_axis_label: SQL Statements
       type: COUNTER
@@ -713,7 +713,7 @@ layers:
       how_to_use: This high-level metric reflects workload volume. Monitor this metric to identify abnormal application behavior or patterns over time. If abnormal patterns emerge, apply the metric's time range to the SQL Activity pages to investigate interesting outliers or patterns. For example, on the Transactions page and the Statements page, sort on the Execution Count column. To find problematic sessions, on the Sessions page, sort on the Transaction Count column. Find the sessions with high transaction counts and trace back to a user or application.
     - name: sql.routine.insert.started.count.internal
       exported_name: sql_routine_insert_started_count_internal
-      labeled_name: 'sql.count{query_type: routine-started-insert, query_internal: true}'
+      labeled_name: 'sql.started.count{query_type: routine-started-insert, query_internal: true}'
       description: Number of SQL INSERT statements started within routine invocation (internal queries)
       y_axis_label: SQL Internal Statements
       type: COUNTER
@@ -742,7 +742,7 @@ layers:
       derivative: NON_NEGATIVE_DERIVATIVE
     - name: sql.routine.select.started.count
       exported_name: sql_routine_select_started_count
-      labeled_name: 'sql.count{query_type: routine-started-select}'
+      labeled_name: 'sql.started.count{query_type: routine-started-select}'
       description: Number of SQL SELECT statements started within routine invocation
       y_axis_label: SQL Statements
       type: COUNTER
@@ -752,7 +752,7 @@ layers:
       how_to_use: This high-level metric reflects workload volume. Monitor this metric to identify abnormal application behavior or patterns over time. If abnormal patterns emerge, apply the metric's time range to the SQL Activity pages to investigate interesting outliers or patterns. For example, on the Transactions page and the Statements page, sort on the Execution Count column. To find problematic sessions, on the Sessions page, sort on the Transaction Count column. Find the sessions with high transaction counts and trace back to a user or application.
     - name: sql.routine.select.started.count.internal
       exported_name: sql_routine_select_started_count_internal
-      labeled_name: 'sql.count{query_type: routine-started-select, query_internal: true}'
+      labeled_name: 'sql.started.count{query_type: routine-started-select, query_internal: true}'
       description: Number of SQL SELECT statements started within routine invocation (internal queries)
       y_axis_label: SQL Internal Statements
       type: COUNTER
@@ -781,7 +781,7 @@ layers:
       derivative: NON_NEGATIVE_DERIVATIVE
     - name: sql.routine.update.started.count
       exported_name: sql_routine_update_started_count
-      labeled_name: 'sql.count{query_type: routine-started-update}'
+      labeled_name: 'sql.started.count{query_type: routine-started-update}'
       description: Number of SQL UPDATE statements started within routine invocation
       y_axis_label: SQL Statements
       type: COUNTER
@@ -791,7 +791,7 @@ layers:
       how_to_use: This high-level metric reflects workload volume. Monitor this metric to identify abnormal application behavior or patterns over time. If abnormal patterns emerge, apply the metric's time range to the SQL Activity pages to investigate interesting outliers or patterns. For example, on the Transactions page and the Statements page, sort on the Execution Count column. To find problematic sessions, on the Sessions page, sort on the Transaction Count column. Find the sessions with high transaction counts and trace back to a user or application.
     - name: sql.routine.update.started.count.internal
       exported_name: sql_routine_update_started_count_internal
-      labeled_name: 'sql.count{query_type: routine-started-update, query_internal: true}'
+      labeled_name: 'sql.started.count{query_type: routine-started-update, query_internal: true}'
       description: Number of SQL UPDATE statements started within routine invocation (internal queries)
       y_axis_label: SQL Internal Statements
       type: COUNTER

pkg/sql/exec_util.go

@@ -1132,7 +1132,7 @@ var (
 		Help:         "Number of SQL SELECT statements started within routine invocation",
 		Measurement:  "SQL Statements",
 		Unit:         metric.Unit_COUNT,
-		LabeledName:  "sql.count",
+		LabeledName:  "sql.started.count",
 		StaticLabels: metric.MakeLabelPairs(metric.LabelQueryType, "routine-started-select"),
 		Category:     metric.Metadata_SQL,
 		HowToUse:     "This high-level metric reflects workload volume. Monitor this metric to identify abnormal application behavior or patterns over time. If abnormal patterns emerge, apply the metric's time range to the SQL Activity pages to investigate interesting outliers or patterns. For example, on the Transactions page and the Statements page, sort on the Execution Count column. To find problematic sessions, on the Sessions page, sort on the Transaction Count column. Find the sessions with high transaction counts and trace back to a user or application.",
@@ -1142,7 +1142,7 @@ var (
 		Help:         "Number of SQL UPDATE statements started within routine invocation",
 		Measurement:  "SQL Statements",
 		Unit:         metric.Unit_COUNT,
-		LabeledName:  "sql.count",
+		LabeledName:  "sql.started.count",
 		StaticLabels: metric.MakeLabelPairs(metric.LabelQueryType, "routine-started-update"),
 		Category:     metric.Metadata_SQL,
 		HowToUse:     "This high-level metric reflects workload volume. Monitor this metric to identify abnormal application behavior or patterns over time. If abnormal patterns emerge, apply the metric's time range to the SQL Activity pages to investigate interesting outliers or patterns. For example, on the Transactions page and the Statements page, sort on the Execution Count column. To find problematic sessions, on the Sessions page, sort on the Transaction Count column. Find the sessions with high transaction counts and trace back to a user or application.",
@@ -1152,7 +1152,7 @@ var (
 		Help:         "Number of SQL INSERT statements started within routine invocation",
 		Measurement:  "SQL Statements",
 		Unit:         metric.Unit_COUNT,
-		LabeledName:  "sql.count",
+		LabeledName:  "sql.started.count",
 		StaticLabels: metric.MakeLabelPairs(metric.LabelQueryType, "routine-started-insert"),
 		Category:     metric.Metadata_SQL,
 		HowToUse:     "This high-level metric reflects workload volume. Monitor this metric to identify abnormal application behavior or patterns over time. If abnormal patterns emerge, apply the metric's time range to the SQL Activity pages to investigate interesting outliers or patterns. For example, on the Transactions page and the Statements page, sort on the Execution Count column. To find problematic sessions, on the Sessions page, sort on the Transaction Count column. Find the sessions with high transaction counts and trace back to a user or application.",
@@ -1162,7 +1162,7 @@ var (
 		Help:         "Number of SQL DELETE statements started within routine invocation",
 		Measurement:  "SQL Statements",
 		Unit:         metric.Unit_COUNT,
-		LabeledName:  "sql.count",
+		LabeledName:  "sql.started.count",
 		StaticLabels: metric.MakeLabelPairs(metric.LabelQueryType, "routine-started-delete"),
 		Category:     metric.Metadata_SQL,
 		HowToUse:     "This high-level metric reflects workload volume. Monitor this metric to identify abnormal application behavior or patterns over time. If abnormal patterns emerge, apply the metric's time range to the SQL Activity pages to investigate interesting outliers or patterns. For example, on the Transactions page and the Statements page, sort on the Execution Count column. To find problematic sessions, on the Sessions page, sort on the Transaction Count column. Find the sessions with high transaction counts and trace back to a user or application.",