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The scale and complexity of workloads in modern cloud services have broughtinto sharper focus a critical challenge in automated index tuning -- the needto recommend high-quality indexes while maintaining index tuning scalability.This challenge is further compounded by the requirement for automated indeximplementations to introduce minimal query performance regressions inproduction deployments, representing a significant barrier to achievingscalability and full automation. This paper directs attention to thesechallenges within automated index tuning and explores ways in which machinelearning (ML) techniques provide new opportunities in their mitigation. Inparticular, we reflect on recent efforts in developing ML techniques forworkload selection, candidate index filtering, speeding up index configurationsearch, reducing the amount of query optimizer calls, and lowering the chancesof performance regressions. We highlight the key takeaways from these effortsand underline the gaps that need to be closed for their effective functioningwithin the traditional index tuning framework. Additionally, we present apreliminary cross-platform design aimed at democratizing index tuning acrossmultiple SQL-like systems -- an imperative in today's continuously expandingdata system landscape. We believe our findings will help provide context andimpetus to the research and development efforts in automated index tuning.

ML-Powered Index Tuning: An Overview of Recent Progress and Open Challenges