SQL metrics

The SQL metrics let you monitor SQL performance.

To view these graphs, select a cluster from the Clusters page, and click Metrics in the Monitoring section of the left side navigation. On the Metrics page, click the SQL tab.

Time interval selection

The time interval selector at the top of each tab allows you to filter the view for a predefined or custom time interval. Use the navigation buttons to move to the previous, next, or current time interval. When you select a time interval, the same interval is selected for all charts on the Metrics page.

SQL Connections

Short Name	CockroachDB Metric Name	Description	Usage
Connections Per Second	sql.new_conns	Number of SQL connections created	The rate of this metric shows how frequently new connections are being established. This can be useful in determining if a high rate of incoming new connections is causing additional load on the server due to a misconfigured application.

SQL Statements

Short Name	CockroachDB Metric Name	Description	Usage
Select	sql.select.count	Number of SQL SELECT statements successfully executed	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 by the Execution Count column. To find problematic sessions, on the Sessions page, sort by the Transaction Count column. Find the sessions with high transaction counts and trace back to a user or application.
Update	sql.update.count	Number of SQL UPDATE statements successfully executed	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 by the Execution Count column. To find problematic sessions, on the Sessions page, sort by the Transaction Count column. Find the sessions with high transaction counts and trace back to a user or application.
Insert	sql.insert.count	Number of SQL INSERT statements successfully executed	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 by the Execution Count column. To find problematic sessions, on the Sessions page, sort by the Transaction Count column. Find the sessions with high transaction counts and trace back to a user or application.
Delete	sql.delete.count	Number of SQL DELETE statements successfully executed	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 by the Execution Count column. To find problematic sessions, on the Sessions page, sort by the Transaction Count column. Find the sessions with high transaction counts and trace back to a user or application.

Service Latency: SQL Statements

Short Name	CockroachDB Metric Name	Description	Usage
P90, P99, P99.9, P99.99	sql.service.latency	Latency of SQL request execution	These high-level metrics reflect workload performance. Monitor these metrics to understand latency over time. If abnormal patterns emerge, apply the metric's time range to the SQL Activity pages to investigate interesting outliers or patterns. The Statements page has P90 Latency and P99 latency columns to enable correlation with this metric.

Connection Latency

Short Name	CockroachDB Metric Name	Description	Usage
P90, P99	sql.conn.latency	Latency to establish and authenticate a SQL connection	Connection latency is calculated as the time in nanoseconds between when the cluster receives a connection request and establishes the connection to the client, including authentication. This graph shows the p90 and p99 latencies for SQL connections to the cluster. These metrics characterize the database connection latency which can affect the application performance, for example, by having slow startup times.

Open SQL Sessions

Short Name	CockroachDB Metric Name	Description	Usage
Connections	sql.conns	Number of open SQL connections	This metric shows the total number of SQL client connections across the cluster. Refer to the Sessions page for more details on the sessions. This metric also shows the distribution, or balancing, of connections across the cluster. Review Connection Pooling.

Open SQL Transactions

Short Name	CockroachDB Metric Name	Description	Usage
Open Transactions	sql.txns.open	Number of currently open user SQL transactions	This metric should roughly correspond to the number of cores * 4. If this metric is consistently larger, scale out the cluster.

Transactions

Short Name	CockroachDB Metric Name	Description	Usage
Begin	sql.txn.begin.count	Number of SQL transaction BEGIN statements successfully executed	This metric reflects workload volume by counting explicit transactions. Use this metric to determine whether explicit transactions can be refactored as implicit transactions (individual statements).
Commits	sql.txn.commit.count	Number of SQL transaction COMMIT statements successfully executed	This metric shows the number of transactions that completed successfully. This metric can be used as a proxy to measure the number of successful explicit transactions.
Rollbacks	sql.txn.rollback.count	Number of SQL transaction ROLLBACK statements successfully executed	This metric shows the number of orderly transaction rollbacks. A persistently high number of rollbacks may negatively impact the workload performance and needs to be investigated.
Aborts	sql.txn.abort.count	Number of SQL transaction abort errors	This high-level metric reflects workload performance. A persistently high number of SQL transaction abort errors may negatively impact the workload performance and needs to be investigated.

Transaction Restarts

Short Name	CockroachDB Metric Name	Description	Usage
Write Too Old	txn.restarts.writetooold	Number of restarts due to a concurrent writer committing first	This metric is one measure of the impact of contention conflicts on workload performance. For guidance on contention conflicts, review transaction contention best practices and performance tuning recipes. Tens of restarts per minute may be a high value, a signal of an elevated degree of contention in the workload, which should be investigated. For the specific error, refer to the transaction retry error reference for more details.
Write Too Old (multiple)	txn.restarts.writetoooldmulti	Number of restarts due to multiple concurrent writers committing first	This metric is one measure of the impact of contention conflicts on workload performance. For guidance on contention conflicts, review transaction contention best practices and performance tuning recipes. Tens of restarts per minute may be a high value, a signal of an elevated degree of contention in the workload, which should be investigated. For the specific error, refer to the transaction retry error reference for more details.
Forwarded Timestamp	txn.restarts.serializable	Number of restarts due to a forwarded commit timestamp and isolation=SERIALIZABLE	This metric is one measure of the impact of contention conflicts on workload performance. For guidance on contention conflicts, review transaction contention best practices and performance tuning recipes. Tens of restarts per minute may be a high value, a signal of an elevated degree of contention in the workload, which should be investigated. For the specific error, refer to the transaction retry error reference for more details.
Async Consensus Failure	txn.restarts.asyncwritefailure	Number of restarts due to async consensus writes that failed to leave intents	This metric is one measure of the impact of contention conflicts on workload performance. For guidance on contention conflicts, review transaction contention best practices and performance tuning recipes. Tens of restarts per minute may be a high value, a signal of an elevated degree of contention in the workload, which should be investigated. For the specific error, refer to the transaction retry error reference for more details.
Read Within Uncertainty Interval	txn.restarts.readwithinuncertainty	Number of restarts due to reading a new value within the uncertainty interval	This metric is one measure of the impact of contention conflicts on workload performance. For guidance on contention conflicts, review transaction contention best practices and performance tuning recipes. Tens of restarts per minute may be a high value, a signal of an elevated degree of contention in the workload, which should be investigated. For the specific error, refer to the transaction retry error reference for more details.
Aborted	txn.restarts.txnaborted	Number of restarts due to an abort by a concurrent transaction (usually due to deadlock)	This metric is one measure of the impact of contention conflicts on workload performance. For guidance on contention conflicts, review transaction contention best practices and performance tuning recipes. Tens of restarts per minute may be a high value, a signal of an elevated degree of contention in the workload, which should be investigated. For the specific error, refer to the transaction retry error reference for more details.
Push Failure	txn.restarts.txnpush	Number of restarts due to a transaction push failure	This metric is one measure of the impact of contention conflicts on workload performance. For guidance on contention conflicts, review transaction contention best practices and performance tuning recipes. Tens of restarts per minute may be a high value, a signal of an elevated degree of contention in the workload, which should be investigated. For the specific error, refer to the transaction retry error reference for more details.
Unknown	txn.restarts.unknown	Number of restarts due to a unknown reasons	The errors tracked by this metric are generally due to deadlocks. Deadlocks can often be prevented with a considered transaction design. Identify the conflicting transactions involved in the deadlocks. Then, if possible, redesign the business logic implementation prone to deadlocks.

Transaction Latency

Short Name	CockroachDB Metric Name	Description	Usage
P90, P99	sql.txn.latency	Latency of SQL transactions	Over the last minute, this cluster executed 90% or 99% of transactions within this time. This time does not include network latency between the cluster and client. These metrics provide an overview of the current SQL workload.

Active SQL Statements

Short Name	CockroachDB Metric Name	Description	Usage
Active Statements	sql.statements.active	Number of currently active user SQL statements	This high-level metric reflects workload volume.

SQL Statement Errors

Short Name	CockroachDB Metric Name	Description	Usage
Errors	sql.failure.count	Number of statements resulting in a planning or runtime error	This metric is a high-level indicator of workload and application degradation with query failures. Use the Insights page to find failed executions with their error code to troubleshoot or use application-level logs, if instrumented, to determine the cause of error.

SQL Statement Contention

Short Name	CockroachDB Metric Name	Description	Usage
Contention	sql.distsql.contended_queries.count	Number of SQL queries that experienced contention	This metric is incremented whenever there is a non-trivial amount of contention experienced by a statement whether read-write or write-write conflicts. Monitor this metric to correlate possible workload performance issues to contention conflicts.

Full Scans

Short Name	CockroachDB Metric Name	Description	Usage
Full scans	sql.full.scan.count	Number of full table or index scans	This metric is a high-level indicator of potentially suboptimal query plans in the workload that may require index tuning and maintenance. To identify the statements with a full table scan, use SHOW FULL TABLE SCAN or the SQL Activity Statements page with the corresponding metric time frame. The Statements page also includes explain plans and index recommendations. You can also disallow full table scans with the disallow_full_table_scans setting. Not all full scans are necessarily bad, especially over smaller tables.

Schema Changes

Short Name	CockroachDB Metric Name	Description	Usage
DDL Statements	sql.ddl.count	Number of SQL DDL statements successfully executed	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 by the Execution Count column. To find problematic sessions, on the Sessions page, sort by the Transaction Count column. Find the sessions with high transaction counts and trace back to a user or application.

Statistics Jobs

Short Name	CockroachDB Metric Name	Description	Usage
Auto Create Statistics Running	jobs.auto_create_stats.currently_running	Number of auto_create_stats jobs currently running in Resume or OnFailOrCancel state	This metric tracks the number of active automatically generated statistics jobs that could also be consuming resources. Ensure that foreground SQL traffic is not impacted by correlating this metric with SQL latency and query volume metrics.
Create Statistics Running	jobs.create_stats.currently_running	Number of create_stats jobs currently running in Resume or OnFailOrCancel state	This metric tracks the number of active create statistics jobs that may be consuming resources. Ensure that foreground SQL traffic is not impacted by correlating this metric with SQL latency and query volume metrics.
Auto Create Statistics Failed	jobs.auto_create_stats.resume_failed	Number of auto_create_stats jobs which failed with a non-retriable error	This metric is a high-level indicator that automatically generated table statistics is failing. Failed statistic creation can lead to the query optimizer running with stale statistics. Stale statistics can cause suboptimal query plans to be selected leading to poor query performance.
Auto Create Statistics Paused	jobs.auto_create_stats.currently_paused	Number of auto_create_stats jobs currently considered Paused	This metric is a high-level indicator that automatically generated statistics jobs are paused which can lead to the query optimizer running with stale statistics. Stale statistics can cause suboptimal query plans to be selected leading to poor query performance.

See also

• Metrics Overview
• Overview metrics tab
• Request Unit metrics
• Changefeed metrics
• Row-Level TTL metrics
• Essential Metrics for Standard