Feature flags: Fix experimental feature flags inventory #7156
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When we collect feature flag properties from all nodes, we start with an empty cluster inventory (a common Erlang recursion pattern). This means that all feature flags are unknown at first. In `merge_feature_flags()`, we must compute a global stability level for each feature flag, in case all nodes are not on the same page (like one nodes considers a feature flag experimental, but another one marks it as stable). That's why we rank stability level: required > stable > experimental. This ranking had one issue: `rabbit_feature_flags:get_stability/1` defaults to `stable` if a feature flag has not explicit stability set. Therefore, with our empty starting inventory, the starting stability would be `stable`. And it would superceed an experimental feature flag stability level, even though all nodes agree on that. Now, if a feature flag is missing from our inventory being collected, we consider its stability level to be experimental. This is different from a known feature flag with no explicit stability level. This way, we are sure that feature flags marked as experimental everywhere will be considered experimental globally.
"~~" was ok when the final string was a format string, but it's not anymore. We need a simple "~".
The return value was incorrectly specified and documented: it can return `undefined` when a feature flag name is passed and that feature flag is unknown. So far, this function is always called with a feature flag properties map, in which case it doesn't return `undefined`.
dcorbacho
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Feature flags: Fix experimental feature flags inventory (backport #7156)
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When we collect feature flag properties from all nodes, we start with an empty cluster inventory (a common Erlang recursion pattern). This means that all feature flags are unknown at first.
In
merge_feature_flags(), we must compute a global stability level for each feature flag, in case all nodes are not on the same page (like one nodes considers a feature flag experimental, but another one marks it as stable). That's why we rank stability level: required > stable > experimental.This ranking had one issue:
rabbit_feature_flags:get_stability/1defaults tostableif a feature flag has not explicit stability set. Therefore, with our empty starting inventory, the starting stability would bestable. And it would superceed an experimental feature flag stability level, even though all nodes agree on that.Now, if a feature flag is missing from our inventory being collected, we consider its stability level to be experimental. This is different from a known feature flag with no explicit stability level. This way, we are sure that feature flags marked as experimental everywhere will be considered experimental globally.
While here, fix two other small issues:
get_stability/1