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143251: sql: decode all plan gists in test builds r=yuzefovich a=yuzefovich
**explain: remove a couple redundant nil checks**
`OutputBuilder.Expr` already checks for `nil` argument, so we can skip doing that in a few places.
**sql: decode all plan gists in test builds**
This commit adds the logic to always decode plan gists in test builds right after the gist was created. This should have trivial overhead while providing extra test coverage for the feature.
Note that we had `TestExplainGist` that was targeting this coverage, so to a certain degree that test is now obsolete. However, I decided to not completely remove it since the test has been good at finding issues unrelated to gists (it is effectively the `sqlsmith` roachtest that runs as a unit test).
Informs: #143211
Epic: None
Release note: None
144790: metric: document metrics in yaml format r=dhartunian a=dhartunian
The generated metrics file is now in YAML format instead of HTML,
which makes it easier to consume with automated tooling.
The file is now structured in a hierarchical format by layer, then
category, then in a list of metric names.
Some new fields have been added:
- `exported_name`: metric name used in the Prometheus endpoint that
ends up shown in 3rd party observability tooling.
- `essential`: boolean that flags a metric as essential for customers
to use.
- `how_to_use`: extended description that is included with essential
metrics
Resolves: #142571
Release note: None
145402: workflows: run automerge on schedule only r=jlinder a=rail
Previously, the automerge workflow was triggered on every push to any branch. This commit changes the workflow to run only on a schedule, also removing the `on: workflow_dispatch` trigger.
Epic: none
Release note: none
Co-authored-by: Yahor Yuzefovich <[email protected]>
Co-authored-by: David Hartunian <[email protected]>
Co-authored-by: Rail Aliiev <[email protected]>
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.",
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