[ML] Improve reason when autoscaling capacity cannot be computed #89316
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When we cannot compute autoscaling capacity we call `MlAutoscalingDeciderService.buildDecisionAndRequestRefresh`. There are 4 callers. 3 of them set their own specific reason before the call. However, the method overwrites the reason with the generic `MEMORY_STALE` msg. This commit addresses this issue which should bubble up more detailed reasons for those edge cases.
dimitris-athanasiou
added
>non-issue
:ml
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Pinging @elastic/ml-core (Team:ML) |
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If this problem exists in 7.17 and the code hasn't diverged too much to make a backport hard then it's probably worth backporting to 7.17.6 too. |
dimitris-athanasiou
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Aug 12, 2022
…stic#89316) When we cannot compute autoscaling capacity we call `MlAutoscalingDeciderService.buildDecisionAndRequestRefresh`. There are 4 callers. 3 of them set their own specific reason before the call. However, the method overwrites the reason with the generic `MEMORY_STALE` msg. This commit addresses this issue which should bubble up more detailed reasons for those edge cases.
💚 Backport successful
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elasticsearchmachine
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Aug 16, 2022
) (#89320) When we cannot compute autoscaling capacity we call `MlAutoscalingDeciderService.buildDecisionAndRequestRefresh`. There are 4 callers. 3 of them set their own specific reason before the call. However, the method overwrites the reason with the generic `MEMORY_STALE` msg. This commit addresses this issue which should bubble up more detailed reasons for those edge cases. Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
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When we cannot compute autoscaling capacity we call
MlAutoscalingDeciderService.buildDecisionAndRequestRefresh.There are 4 callers. 3 of them set their own specific reason before
the call. However, the method overwrites the reason with the generic
MEMORY_STALEmsg. This commit addresses this issue which shouldbubble up more detailed reasons for those edge cases.