. . . . . . 111 2. Pull-based metrics system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 3. Push-based metrics system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 Custom metrics & queries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Target Metrics . . . . . . . . . . . .

. . . . . . 101 2. Pull-based metrics system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 3. Push-based metrics system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Custom metrics & queries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250 Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Target Metrics . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 Custom metrics & queries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290 Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 Target Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 Source Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . 92 2. Pull-based metrics system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 3. Push-based metrics system . . . . . . . . . . . . . Pull-based metrics system Vitess uses Go’s expvar package to expose various metrics, with the expectation that a user can configure a pull-based metrics system to ingest those metrics. Metrics are published published to http://:/debug/vars as JSON key-value pairs, which should be easy for any metrics system to parse. Scraping Vitess variables is a good way to integrate Vitess into an existing monitoring

throttler begins in dormant probe mode. As long as no application or client is actually looking for metrics, it probes the servers at multi-second intervals. – When applications check for throttle advice tablets. This is the cluster’s metric . The cluster’s metric is only as accurate as the following metrics: • The probe interval • The heartbeat injection interval • The aggregation interval The error margin -queryserver-config-enable- table-acl-dry-run If this flag is enabled, tabletserver will emit monitoring metrics and let the request pass regardless of table acl check results -queryserver-config-idle-timeout int

-queryserver-config-enable-table- acl-dry-run If this flag is enabled, tabletserver will emit monitoring metrics and let the request pass regardless of table acl check results -queryserver-config-idle-timeout int -queryserver-config-enable-table- acl-dry-run boolean If this flag is enabled, tabletserver will emit monitoring metrics and let the request pass regardless of table acl check results -queryserver-config-idle-timeout int -queryserver-config-enable-table- acl-dry-run If this flag is enabled, tabletserver will emit monitoring metrics and let the request pass regardless of table acl check results -queryserver-config-idle-timeout int

The following option may be useful: * -queryserver-config-enable-table-acl-dry-run: Only emit metrics when an ACL denies a request, but let the actual request pass through successfully. This allows you