laserninja opened a new issue, #13335:
URL: https://github.com/apache/gravitino/issues/13335

   ### Describe the feature
   
   Extend the Table Maintenance Service to use measured maintenance outcomes 
when selecting future actions, prioritizing expected workload benefit within a 
configured compute budget.
   
   ### Motivation
   
   The optimizer already scores maintenance candidates and evaluates 
before-and-after metrics. Connecting these capabilities through persistent 
decision history would help avoid repeatedly performing expensive maintenance 
with little measurable benefit and make recommendations explainable.
   
   ### Describe the solution
   
   - Persist each recommendation’s input statistics, strategy, estimated 
resource cost, execution identity, and measured outcome.
   - Implement an opt-in selector that uses historical outcomes to rank 
eligible actions within a defined resource budget.
   - Start with Iceberg compaction and one query engine, using comparable 
workload metrics and maintenance compute consumption.
   - Include cooldowns, concurrency limits, and explicit handling of missing or 
inconclusive measurements.
   - Provide explanations for selected and deferred actions, including 
estimated benefit, cost, and uncertainty.
   - Support shadow mode to compare recommendations with existing policies 
before enabling execution.
   
   Validate with repeatable workloads comparing resource consumption and 
workload performance against the existing selector. Account for caching and 
workload changes rather than treating every before-and-after improvement as 
causal.
   
   ### Additional context
   
   Complements [#11851](https://github.com/apache/gravitino/issues/11851), 
which proposes the Optimizer Service runtime and REST APIs. This feature 
focuses on decision quality and feedback, building on the existing recommender 
and monitor extension points.
   


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