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These essential CockroachDB metrics let you monitor your CockroachDB Standard cluster. The metrics are available on graphs on the tabs listed in the Metrics tabs column. Where Custom is listed, the metric may be graphed in a Custom Metrics Chart. The Usage column explains why each metric is important to visualize and how to make both practical and actionable use of the metric in a production deployment.
SQL
| CockroachDB Metric Name | Short Name | Description | Usage | Metrics tab |
sql.conn.latency |
P90, P99 | 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. |
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sql.conns |
Connections | 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. |
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sql.ddl.count |
DDL Statements | 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. | |
sql.delete.count |
Delete | 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. | |
sql.distsql.contended_queries.count |
Contention | 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. | |
sql.failure.count |
Errors | 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.full.scan.count |
Full scans | 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. |
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sql.insert.count |
Insert | 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. | |
sql.new_conns |
Connections Per Second | 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.select.count |
Select | 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. | |
sql.service.latency |
P90, P99, P99.9, P99.99 | 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. | |
sql.statements.active |
Active Statements | Number of currently active user SQL statements | This high-level metric reflects workload volume. | |
sql.txn.abort.count |
Aborts | 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. | |
sql.txn.begin.count |
Begin | 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). | |
sql.txn.commit.count |
Commits | 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. | |
sql.txn.latency |
P90, P99 | 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. | |
sql.txn.rollback.count |
Rollbacks | 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. | |
sql.txns.open |
Open Transactions | 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. | |
sql.update.count |
Update | 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. | |
txn.restarts.asyncwritefailure |
Async Consensus Failure | 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. | |
txn.restarts.readwithinuncertainty |
Read Within Uncertainty Interval | 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. | |
txn.restarts.serializable |
Forwarded Timestamp | 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. | |
txn.restarts.txnaborted |
Aborted | 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. | |
txn.restarts.txnpush |
Push Failure | 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. | |
txn.restarts.unknown |
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. | |
txn.restarts.writetooold |
Write Too Old | 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. | |
txn.restarts.writetoooldmulti |
Write Too Old (multiple) | 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. |
Storage
| CockroachDB Metric Name | Short Name | Description | Usage | Metrics tab |
livebytes |
Storage usage | Number of bytes of live data (keys plus values) | The amount of data being stored in the cluster. In the Storage Used graph, this is the logical number of live bytes and does not account for compression or replication. |
Table Statistics
| CockroachDB Metric Name | Short Name | Description | Usage | Metrics tab |
jobs.auto_create_stats.currently_paused |
Auto Create Statistics 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. |
SQL, |
jobs.auto_create_stats.currently_running |
Auto Create Statistics 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. |
SQL, |
jobs.auto_create_stats.resume_failed |
Auto Create Statistics 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. |
SQL, |
jobs.create_stats.currently_running |
Create Statistics 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. |
SQL, |
Changefeeds
| CockroachDB Metric Name | Short Name | Description | Usage | Metrics tab |
changefeed.commit_latency |
P99, P90 | Event commit latency: a difference between event MVCC timestamp and the time it was acknowledged by the downstream sink. If the sink batches events, then the difference between the oldest event in the batch and acknowledgement is recorded; Excludes latency during backfill | This metric provides useful context when assessing the state of changefeeds. This metric characterizes the end-to-end lag between a committed change and that change applied at the destination. | |
changefeed.emitted_bytes |
Emitted bytes | Bytes emitted by all feeds | This metric provides a useful context when assessing the state of changefeeds. This metric characterizes the throughput bytes being streamed from the CockroachDB cluster. | |
changefeed.emitted_messages |
Emitted messages | Messages emitted by all feeds | This metric provides a useful context when assessing the state of changefeeds. This metric characterizes the rate of changes being streamed from the CockroachDB cluster. | |
changefeed.error_retries |
Errors | Total retryable errors encountered by all changefeeds | This metric tracks transient changefeed errors. Alert on "too many" errors, such as 50 retries in 15 minutes. For example, during a rolling upgrade this counter will increase because the changefeed jobs will restart following node restarts. There is an exponential backoff, up to 10 minutes. But if there is no rolling upgrade in process or other cluster maintenance, and the error rate is high, investigate the changefeed job. | |
changefeed.failures |
Failures | Total number of changefeed jobs which have failed | This metric tracks the permanent changefeed job failures that the jobs system will not try to restart. Any increase in this counter should be investigated. An alert on this metric is recommended. | |
changefeed.running |
Running | Number of currently running changefeeds, including sinkless | This metric tracks the total number of all running changefeeds. | |
jobs.changefeed.currently_paused |
Paused | Number of changefeed jobs currently considered Paused | Monitor and alert on this metric to safeguard against an inadvertent operational error of leaving a changefeed job in a paused state for an extended period of time. Changefeed jobs should not be paused for a long time because the protected timestamp prevents garbage collection. | |
jobs.changefeed.protected_age_sec |
Protected Timestamp Age | The age of the oldest PTS record protected by changefeed jobs | changefeeds use protected timestamps to protect the data from being garbage collected. Ensure the protected timestamp age does not significantly exceed the GC TTL zone configuration. Alert on this metric if the protected timestamp age is greater than 3 times the GC TTL. |
Row-Level TTL
| CockroachDB Metric Name | Short Name | Description | Usage | Metrics tab |
jobs.row_level_ttl.currently_paused |
Paused | Number of row_level_ttl jobs currently considered Paused | Monitor this metric to ensure the Row-Level TTL job does not remain paused inadvertently for an extended period. | |
jobs.row_level_ttl.currently_running |
Running | Number of row_level_ttl jobs currently running in Resume or OnFailOrCancel state | Monitor this metric to ensure there are not too many Row-Level TTL jobs running at the same time. Generally, this metric should be in the low single digits. | |
jobs.row_level_ttl.delete_duration |
Delete Latency (P90), Delete Latency (P99) | Duration for delete requests during row level TTL. | See Description. | |
jobs.row_level_ttl.resume_completed |
Resume Completed | Number of row_level_ttl jobs which successfully resumed to completion | If Row-Level TTL is enabled, this metric should be nonzero and correspond to the ttl_cron setting that was chosen. If this metric is zero, it means the job is not running. |
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jobs.row_level_ttl.resume_failed |
Resume Failed | Number of row_level_ttl jobs which failed with a non-retriable error | This metric should remain at zero. Repeated errors means the Row-Level TTL job is not deleting data. | |
jobs.row_level_ttl.rows_deleted |
Rows deleted | Number of rows deleted by the row level TTL job. | Correlate this metric with the metric jobs.row_level_ttl.rows_selected to ensure all the rows that should be deleted are actually getting deleted. |
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jobs.row_level_ttl.rows_selected |
Rows selected | Number of rows selected for deletion by the row level TTL job. | Correlate this metric with the metric jobs.row_level_ttl.rows_deleted to ensure all the rows that should be deleted are actually getting deleted. |
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jobs.row_level_ttl.select_duration |
Select Latency (P90), Select Latency (P99) | Duration for select requests during row level TTL. | See Description. | |
jobs.row_level_ttl.total_expired_rows |
Expired Rows | Approximate number of rows that have expired the TTL on the TTL table. | The approximate number of rows that have expired the TTL on all TTL tables. | |
jobs.row_level_ttl.total_rows |
Total Rows | Approximate number of rows on the TTL table. | The approximate number of rows on all TTL tables. | |
schedules.scheduled-row-level-ttl-executor.failed |
Failed Schedules | Number of scheduled-row-level-ttl-executor jobs failed | Monitor this metric to ensure the Row-Level TTL job is running. If it is non-zero, it means the job could not be created. |
See also
- Metrics Overview
- Overview metrics
- Request Unit metrics
- SQL metrics
- Changefeed metrics
- Row-Level TTL metrics
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