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metric: add labels for sql.*.started.count
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. Ref #143536 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. <what was there before: Previously, ...> <why it needed to change: This was inadequate because ...> <what you did about it: To address this, this patch ...>
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‎docs/generated/metrics/metrics.yaml‎

Lines changed: 76 additions & 64 deletions
Original file line numberDiff line numberDiff line change
@@ -518,6 +518,25 @@ layers:
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unit: COUNT
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aggregation: AVG
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derivative: NON_NEGATIVE_DERIVATIVE
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- name: sql.delete.started.count
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exported_name: sql_delete_started_count
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labeled_name: 'sql.started.count{query_type: delete}'
524+
description: Number of SQL DELETE statements started
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y_axis_label: SQL Statements
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type: COUNTER
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unit: COUNT
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aggregation: AVG
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derivative: NON_NEGATIVE_DERIVATIVE
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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.
531+
- name: sql.delete.started.count.internal
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exported_name: sql_delete_started_count_internal
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labeled_name: 'sql.started.count{query_type: delete, query_internal: true}'
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description: Number of SQL DELETE statements started (internal queries)
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y_axis_label: SQL Internal Statements
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type: COUNTER
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unit: COUNT
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aggregation: AVG
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derivative: NON_NEGATIVE_DERIVATIVE
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- name: sql.distsql.contended_queries.count
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exported_name: sql_distsql_contended_queries_count
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description: Number of SQL queries that experienced contention
@@ -584,6 +603,25 @@ layers:
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unit: COUNT
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aggregation: AVG
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derivative: NON_NEGATIVE_DERIVATIVE
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- name: sql.insert.started.count
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exported_name: sql_insert_started_count
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labeled_name: 'sql.started.count{query_type: insert}'
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description: Number of SQL INSERT statements started
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y_axis_label: SQL Statements
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type: COUNTER
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unit: COUNT
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aggregation: AVG
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derivative: NON_NEGATIVE_DERIVATIVE
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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.
616+
- name: sql.insert.started.count.internal
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exported_name: sql_insert_started_count_internal
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labeled_name: 'sql.started.count{query_type: insert, query_internal: true}'
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description: Number of SQL INSERT statements started (internal queries)
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y_axis_label: SQL Internal Statements
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type: COUNTER
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unit: COUNT
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aggregation: AVG
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derivative: NON_NEGATIVE_DERIVATIVE
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- name: sql.mem.root.current
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exported_name: sql_mem_root_current
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description: Current sql statement memory usage for root
@@ -780,6 +818,25 @@ layers:
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unit: COUNT
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aggregation: AVG
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derivative: NON_NEGATIVE_DERIVATIVE
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- name: sql.select.started.count
822+
exported_name: sql_select_started_count
823+
labeled_name: 'sql.started.count{query_type: select}'
824+
description: Number of SQL SELECT statements started
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y_axis_label: SQL Statements
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type: COUNTER
827+
unit: COUNT
828+
aggregation: AVG
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derivative: NON_NEGATIVE_DERIVATIVE
830+
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.
831+
- name: sql.select.started.count.internal
832+
exported_name: sql_select_started_count_internal
833+
labeled_name: 'sql.started.count{query_type: select, query_internal: true}'
834+
description: Number of SQL SELECT statements started (internal queries)
835+
y_axis_label: SQL Internal Statements
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type: COUNTER
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unit: COUNT
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aggregation: AVG
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derivative: NON_NEGATIVE_DERIVATIVE
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- name: sql.service.latency
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exported_name: sql_service_latency
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description: Latency of SQL request execution
@@ -944,6 +1001,25 @@ layers:
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unit: COUNT
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aggregation: AVG
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derivative: NON_NEGATIVE_DERIVATIVE
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- name: sql.update.started.count
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exported_name: sql_update_started_count
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labeled_name: 'sql.started.count{query_type: update}'
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description: Number of SQL UPDATE statements started
1008+
y_axis_label: SQL Statements
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type: COUNTER
1010+
unit: COUNT
1011+
aggregation: AVG
1012+
derivative: NON_NEGATIVE_DERIVATIVE
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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.
1014+
- name: sql.update.started.count.internal
1015+
exported_name: sql_update_started_count_internal
1016+
labeled_name: 'sql.started.count{query_type: update, query_internal: true}'
1017+
description: Number of SQL UPDATE statements started (internal queries)
1018+
y_axis_label: SQL Internal Statements
1019+
type: COUNTER
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unit: COUNT
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aggregation: AVG
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derivative: NON_NEGATIVE_DERIVATIVE
9471023
- name: txn.restarts.serializable
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exported_name: txn_restarts_serializable
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description: Number of restarts due to a forwarded commit timestamp and isolation=SERIALIZABLE
@@ -7717,22 +7793,6 @@ layers:
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unit: COUNT
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aggregation: AVG
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derivative: NON_NEGATIVE_DERIVATIVE
7720-
- name: sql.delete.started.count
7721-
exported_name: sql_delete_started_count
7722-
description: Number of SQL DELETE statements started
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y_axis_label: SQL Statements
7724-
type: COUNTER
7725-
unit: COUNT
7726-
aggregation: AVG
7727-
derivative: NON_NEGATIVE_DERIVATIVE
7728-
- name: sql.delete.started.count.internal
7729-
exported_name: sql_delete_started_count_internal
7730-
description: Number of SQL DELETE statements started (internal queries)
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y_axis_label: SQL Internal Statements
7732-
type: COUNTER
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unit: COUNT
7734-
aggregation: AVG
7735-
derivative: NON_NEGATIVE_DERIVATIVE
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- name: sql.disk.distsql.current
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exported_name: sql_disk_distsql_current
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description: Current sql statement disk usage for distsql
@@ -8157,22 +8217,6 @@ layers:
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unit: COUNT
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aggregation: AVG
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derivative: NON_NEGATIVE_DERIVATIVE
8160-
- name: sql.insert.started.count
8161-
exported_name: sql_insert_started_count
8162-
description: Number of SQL INSERT statements started
8163-
y_axis_label: SQL Statements
8164-
type: COUNTER
8165-
unit: COUNT
8166-
aggregation: AVG
8167-
derivative: NON_NEGATIVE_DERIVATIVE
8168-
- name: sql.insert.started.count.internal
8169-
exported_name: sql_insert_started_count_internal
8170-
description: Number of SQL INSERT statements started (internal queries)
8171-
y_axis_label: SQL Internal Statements
8172-
type: COUNTER
8173-
unit: COUNT
8174-
aggregation: AVG
8175-
derivative: NON_NEGATIVE_DERIVATIVE
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- name: sql.insights.anomaly_detection.evictions
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exported_name: sql_insights_anomaly_detection_evictions
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description: Evictions of fingerprint latency summaries due to memory pressure
@@ -8837,22 +8881,6 @@ layers:
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unit: COUNT
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aggregation: AVG
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derivative: NONE
8840-
- name: sql.select.started.count
8841-
exported_name: sql_select_started_count
8842-
description: Number of SQL SELECT statements started
8843-
y_axis_label: SQL Statements
8844-
type: COUNTER
8845-
unit: COUNT
8846-
aggregation: AVG
8847-
derivative: NON_NEGATIVE_DERIVATIVE
8848-
- name: sql.select.started.count.internal
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exported_name: sql_select_started_count_internal
8850-
description: Number of SQL SELECT statements started (internal queries)
8851-
y_axis_label: SQL Internal Statements
8852-
type: COUNTER
8853-
unit: COUNT
8854-
aggregation: AVG
8855-
derivative: NON_NEGATIVE_DERIVATIVE
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- name: sql.service.latency.consistent
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exported_name: sql_service_latency_consistent
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description: Latency of SQL request execution of non-historical queries
@@ -9293,22 +9321,6 @@ layers:
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unit: COUNT
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aggregation: AVG
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derivative: NON_NEGATIVE_DERIVATIVE
9296-
- name: sql.update.started.count
9297-
exported_name: sql_update_started_count
9298-
description: Number of SQL UPDATE statements started
9299-
y_axis_label: SQL Statements
9300-
type: COUNTER
9301-
unit: COUNT
9302-
aggregation: AVG
9303-
derivative: NON_NEGATIVE_DERIVATIVE
9304-
- name: sql.update.started.count.internal
9305-
exported_name: sql_update_started_count_internal
9306-
description: Number of SQL UPDATE statements started (internal queries)
9307-
y_axis_label: SQL Internal Statements
9308-
type: COUNTER
9309-
unit: COUNT
9310-
aggregation: AVG
9311-
derivative: NON_NEGATIVE_DERIVATIVE
93129324
- name: sql.vecindex.pending_splits_merges
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exported_name: sql_vecindex_pending_splits_merges
93149326
description: Total number of vector index splits and merges waiting to be processed

‎pkg/sql/exec_util.go‎

Lines changed: 32 additions & 16 deletions
Original file line numberDiff line numberDiff line change
@@ -1016,28 +1016,44 @@ var (
10161016
Unit: metric.Unit_COUNT,
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}
10181018
MetaSelectStarted = metric.Metadata{
1019-
Name: "sql.select.started.count",
1020-
Help: "Number of SQL SELECT statements started",
1021-
Measurement: "SQL Statements",
1022-
Unit: metric.Unit_COUNT,
1019+
Name: "sql.select.started.count",
1020+
Help: "Number of SQL SELECT statements started",
1021+
Measurement: "SQL Statements",
1022+
Unit: metric.Unit_COUNT,
1023+
LabeledName: "sql.started.count",
1024+
StaticLabels: metric.MakeLabelPairs(metric.LabelQueryType, "select"),
1025+
Category: metric.Metadata_SQL,
1026+
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.",
10231027
}
10241028
MetaUpdateStarted = metric.Metadata{
1025-
Name: "sql.update.started.count",
1026-
Help: "Number of SQL UPDATE statements started",
1027-
Measurement: "SQL Statements",
1028-
Unit: metric.Unit_COUNT,
1029+
Name: "sql.update.started.count",
1030+
Help: "Number of SQL UPDATE statements started",
1031+
Measurement: "SQL Statements",
1032+
Unit: metric.Unit_COUNT,
1033+
LabeledName: "sql.started.count",
1034+
StaticLabels: metric.MakeLabelPairs(metric.LabelQueryType, "update"),
1035+
Category: metric.Metadata_SQL,
1036+
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.",
10291037
}
10301038
MetaInsertStarted = metric.Metadata{
1031-
Name: "sql.insert.started.count",
1032-
Help: "Number of SQL INSERT statements started",
1033-
Measurement: "SQL Statements",
1034-
Unit: metric.Unit_COUNT,
1039+
Name: "sql.insert.started.count",
1040+
Help: "Number of SQL INSERT statements started",
1041+
Measurement: "SQL Statements",
1042+
Unit: metric.Unit_COUNT,
1043+
LabeledName: "sql.started.count",
1044+
StaticLabels: metric.MakeLabelPairs(metric.LabelQueryType, "insert"),
1045+
Category: metric.Metadata_SQL,
1046+
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.",
10351047
}
10361048
MetaDeleteStarted = metric.Metadata{
1037-
Name: "sql.delete.started.count",
1038-
Help: "Number of SQL DELETE statements started",
1039-
Measurement: "SQL Statements",
1040-
Unit: metric.Unit_COUNT,
1049+
Name: "sql.delete.started.count",
1050+
Help: "Number of SQL DELETE statements started",
1051+
Measurement: "SQL Statements",
1052+
Unit: metric.Unit_COUNT,
1053+
LabeledName: "sql.started.count",
1054+
StaticLabels: metric.MakeLabelPairs(metric.LabelQueryType, "delete"),
1055+
Category: metric.Metadata_SQL,
1056+
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.",
10411057
}
10421058
MetaCRUDStarted = metric.Metadata{
10431059
Name: "sql.crud_query.started.count",

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