Merge #151946

151946: metric: add labels for `sql.*.started.count` r=ZhouXing19 a=ZhouXing19

Previously, only metrics for *executed* `SELECT|UPDATE|DELETE|INSERT` statements are labeled, while their *started* equivalent are not labeled. This commit is to label these metrics. Since they are not frequently used metrics, they are not marked as Essential.


Fixes: #152074

Release note (ops change): `sql.select.started.count`, `sql.insert.started.count`, `sql.update.started.count`, `sql.delete.started.count` are now labeled with `sql.started.count`.

Co-authored-by: ZhouXing19 <[email protected]>

Author: craig[bot]

docs/generated/metrics/metrics.yaml

@@ -518,6 +518,25 @@ layers:
       unit: COUNT
       aggregation: AVG
       derivative: NON_NEGATIVE_DERIVATIVE
+    - name: sql.delete.started.count
+      exported_name: sql_delete_started_count
+      labeled_name: 'sql.started.count{query_type: delete}'
+      description: Number of SQL DELETE statements started
+      y_axis_label: SQL Statements
+      type: COUNTER
+      unit: COUNT
+      aggregation: AVG
+      derivative: NON_NEGATIVE_DERIVATIVE
+      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.delete.started.count.internal
+      exported_name: sql_delete_started_count_internal
+      labeled_name: 'sql.started.count{query_type: delete, query_internal: true}'
+      description: Number of SQL DELETE statements started (internal queries)
+      y_axis_label: SQL Internal Statements
+      type: COUNTER
+      unit: COUNT
+      aggregation: AVG
+      derivative: NON_NEGATIVE_DERIVATIVE
     - name: sql.distsql.contended_queries.count
       exported_name: sql_distsql_contended_queries_count
       description: Number of SQL queries that experienced contention
@@ -584,6 +603,25 @@ layers:
       unit: COUNT
       aggregation: AVG
       derivative: NON_NEGATIVE_DERIVATIVE
+    - name: sql.insert.started.count
+      exported_name: sql_insert_started_count
+      labeled_name: 'sql.started.count{query_type: insert}'
+      description: Number of SQL INSERT statements started
+      y_axis_label: SQL Statements
+      type: COUNTER
+      unit: COUNT
+      aggregation: AVG
+      derivative: NON_NEGATIVE_DERIVATIVE
+      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.insert.started.count.internal
+      exported_name: sql_insert_started_count_internal
+      labeled_name: 'sql.started.count{query_type: insert, query_internal: true}'
+      description: Number of SQL INSERT statements started (internal queries)
+      y_axis_label: SQL Internal Statements
+      type: COUNTER
+      unit: COUNT
+      aggregation: AVG
+      derivative: NON_NEGATIVE_DERIVATIVE
     - name: sql.mem.root.current
       exported_name: sql_mem_root_current
       description: Current sql statement memory usage for root
@@ -780,6 +818,25 @@ layers:
       unit: COUNT
       aggregation: AVG
       derivative: NON_NEGATIVE_DERIVATIVE
+    - name: sql.select.started.count
+      exported_name: sql_select_started_count
+      labeled_name: 'sql.started.count{query_type: select}'
+      description: Number of SQL SELECT statements started
+      y_axis_label: SQL Statements
+      type: COUNTER
+      unit: COUNT
+      aggregation: AVG
+      derivative: NON_NEGATIVE_DERIVATIVE
+      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.select.started.count.internal
+      exported_name: sql_select_started_count_internal
+      labeled_name: 'sql.started.count{query_type: select, query_internal: true}'
+      description: Number of SQL SELECT statements started (internal queries)
+      y_axis_label: SQL Internal Statements
+      type: COUNTER
+      unit: COUNT
+      aggregation: AVG
+      derivative: NON_NEGATIVE_DERIVATIVE
     - name: sql.service.latency
       exported_name: sql_service_latency
       description: Latency of SQL request execution
@@ -944,6 +1001,25 @@ layers:
       unit: COUNT
       aggregation: AVG
       derivative: NON_NEGATIVE_DERIVATIVE
+    - name: sql.update.started.count
+      exported_name: sql_update_started_count
+      labeled_name: 'sql.started.count{query_type: update}'
+      description: Number of SQL UPDATE statements started
+      y_axis_label: SQL Statements
+      type: COUNTER
+      unit: COUNT
+      aggregation: AVG
+      derivative: NON_NEGATIVE_DERIVATIVE
+      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.update.started.count.internal
+      exported_name: sql_update_started_count_internal
+      labeled_name: 'sql.started.count{query_type: update, query_internal: true}'
+      description: Number of SQL UPDATE statements started (internal queries)
+      y_axis_label: SQL Internal Statements
+      type: COUNTER
+      unit: COUNT
+      aggregation: AVG
+      derivative: NON_NEGATIVE_DERIVATIVE
     - name: txn.restarts.serializable
       exported_name: txn_restarts_serializable
       description: Number of restarts due to a forwarded commit timestamp and isolation=SERIALIZABLE
@@ -7717,22 +7793,6 @@ layers:
       unit: COUNT
       aggregation: AVG
       derivative: NON_NEGATIVE_DERIVATIVE
-    - name: sql.delete.started.count
-      exported_name: sql_delete_started_count
-      description: Number of SQL DELETE statements started
-      y_axis_label: SQL Statements
-      type: COUNTER
-      unit: COUNT
-      aggregation: AVG
-      derivative: NON_NEGATIVE_DERIVATIVE
-    - name: sql.delete.started.count.internal
-      exported_name: sql_delete_started_count_internal
-      description: Number of SQL DELETE statements started (internal queries)
-      y_axis_label: SQL Internal Statements
-      type: COUNTER
-      unit: COUNT
-      aggregation: AVG
-      derivative: NON_NEGATIVE_DERIVATIVE
     - name: sql.disk.distsql.current
       exported_name: sql_disk_distsql_current
       description: Current sql statement disk usage for distsql
@@ -8157,22 +8217,6 @@ layers:
       unit: COUNT
       aggregation: AVG
       derivative: NON_NEGATIVE_DERIVATIVE
-    - name: sql.insert.started.count
-      exported_name: sql_insert_started_count
-      description: Number of SQL INSERT statements started
-      y_axis_label: SQL Statements
-      type: COUNTER
-      unit: COUNT
-      aggregation: AVG
-      derivative: NON_NEGATIVE_DERIVATIVE
-    - name: sql.insert.started.count.internal
-      exported_name: sql_insert_started_count_internal
-      description: Number of SQL INSERT statements started (internal queries)
-      y_axis_label: SQL Internal Statements
-      type: COUNTER
-      unit: COUNT
-      aggregation: AVG
-      derivative: NON_NEGATIVE_DERIVATIVE
     - name: sql.insights.anomaly_detection.evictions
       exported_name: sql_insights_anomaly_detection_evictions
       description: Evictions of fingerprint latency summaries due to memory pressure
@@ -8837,22 +8881,6 @@ layers:
       unit: COUNT
       aggregation: AVG
       derivative: NONE
-    - name: sql.select.started.count
-      exported_name: sql_select_started_count
-      description: Number of SQL SELECT statements started
-      y_axis_label: SQL Statements
-      type: COUNTER
-      unit: COUNT
-      aggregation: AVG
-      derivative: NON_NEGATIVE_DERIVATIVE
-    - name: sql.select.started.count.internal
-      exported_name: sql_select_started_count_internal
-      description: Number of SQL SELECT statements started (internal queries)
-      y_axis_label: SQL Internal Statements
-      type: COUNTER
-      unit: COUNT
-      aggregation: AVG
-      derivative: NON_NEGATIVE_DERIVATIVE
     - name: sql.service.latency.consistent
       exported_name: sql_service_latency_consistent
       description: Latency of SQL request execution of non-historical queries
@@ -9293,22 +9321,6 @@ layers:
       unit: COUNT
       aggregation: AVG
       derivative: NON_NEGATIVE_DERIVATIVE
-    - name: sql.update.started.count
-      exported_name: sql_update_started_count
-      description: Number of SQL UPDATE statements started
-      y_axis_label: SQL Statements
-      type: COUNTER
-      unit: COUNT
-      aggregation: AVG
-      derivative: NON_NEGATIVE_DERIVATIVE
-    - name: sql.update.started.count.internal
-      exported_name: sql_update_started_count_internal
-      description: Number of SQL UPDATE statements started (internal queries)
-      y_axis_label: SQL Internal Statements
-      type: COUNTER
-      unit: COUNT
-      aggregation: AVG
-      derivative: NON_NEGATIVE_DERIVATIVE
     - name: sql.vecindex.pending_splits_merges
       exported_name: sql_vecindex_pending_splits_merges
       description: Total number of vector index splits and merges waiting to be processed

pkg/sql/exec_util.go

@@ -1016,28 +1016,44 @@ var (
 		Unit:        metric.Unit_COUNT,
 	}
 	MetaSelectStarted = metric.Metadata{
-		Name:        "sql.select.started.count",
-		Help:        "Number of SQL SELECT statements started",
-		Measurement: "SQL Statements",
-		Unit:        metric.Unit_COUNT,
+		Name:         "sql.select.started.count",
+		Help:         "Number of SQL SELECT statements started",
+		Measurement:  "SQL Statements",
+		Unit:         metric.Unit_COUNT,
+		LabeledName:  "sql.started.count",
+		StaticLabels: metric.MakeLabelPairs(metric.LabelQueryType, "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.",
 	}
 	MetaUpdateStarted = metric.Metadata{
-		Name:        "sql.update.started.count",
-		Help:        "Number of SQL UPDATE statements started",
-		Measurement: "SQL Statements",
-		Unit:        metric.Unit_COUNT,
+		Name:         "sql.update.started.count",
+		Help:         "Number of SQL UPDATE statements started",
+		Measurement:  "SQL Statements",
+		Unit:         metric.Unit_COUNT,
+		LabeledName:  "sql.started.count",
+		StaticLabels: metric.MakeLabelPairs(metric.LabelQueryType, "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.",
 	}
 	MetaInsertStarted = metric.Metadata{
-		Name:        "sql.insert.started.count",
-		Help:        "Number of SQL INSERT statements started",
-		Measurement: "SQL Statements",
-		Unit:        metric.Unit_COUNT,
+		Name:         "sql.insert.started.count",
+		Help:         "Number of SQL INSERT statements started",
+		Measurement:  "SQL Statements",
+		Unit:         metric.Unit_COUNT,
+		LabeledName:  "sql.started.count",
+		StaticLabels: metric.MakeLabelPairs(metric.LabelQueryType, "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.",
 	}
 	MetaDeleteStarted = metric.Metadata{
-		Name:        "sql.delete.started.count",
-		Help:        "Number of SQL DELETE statements started",
-		Measurement: "SQL Statements",
-		Unit:        metric.Unit_COUNT,
+		Name:         "sql.delete.started.count",
+		Help:         "Number of SQL DELETE statements started",
+		Measurement:  "SQL Statements",
+		Unit:         metric.Unit_COUNT,
+		LabeledName:  "sql.started.count",
+		StaticLabels: metric.MakeLabelPairs(metric.LabelQueryType, "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.",
 	}
 	MetaCRUDStarted = metric.Metadata{
 		Name:        "sql.crud_query.started.count",