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152365: sql: mark a few recently added metrics as non-essential r=yuzefovich a=yuzefovich
I think it was a mistake that we marked "routine started" metrics as essential - we only do that for "executed" metrics outside of the routine context, and not for "started", so I think we should follow that pattern.
Additionally, this commit moves some metrics around so that "started" and "executed" are grouped.
Epic: CRDB-52656
Release note: None
Co-authored-by: Yahor Yuzefovich <[email protected]>
Copy file name to clipboardExpand all lines: docs/generated/metrics/metrics.yaml
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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.
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.
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.
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.
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