Esc: An Early-stopping Checker for Budget-aware Index Tuning

2025-05-05

AI Summary

Existing budget-aware index tuning systems suffer from prohibitively high “what-if” optimizer invocation costs and diminishing returns in later optimization stages. This paper proposes the first early-stopping paradigm tailored to this scenario, introducing Esc—a lightweight early-stopper that enables quality-controllable automatic termination via (i) query cost prediction modeling, (ii) quality-loss estimation under remaining budget, (iii) incremental configuration evaluation, and (iv) low-overhead runtime monitoring. Its core innovation lies in explicitly modeling index quality loss as the primary early-stopping criterion, guaranteeing performance within user-specified tolerance thresholds. Experiments on industrial benchmarks and real customer workloads demonstrate that Esc reduces “what-if” calls by up to 67%, achieves near-zero average quality loss, and incurs negligible overhead.

Technology Category

• Search and Optimization: Algorithm Configuration
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Search

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Index tuning is a time-consuming process. One major performance bottleneck in existing index tuning systems is the large amount of"what-if"query optimizer calls that estimate the cost of a given pair of query and index configuration without materializing the indexes. There has been recent work on budget-aware index tuning that limits the amount of what-if calls allowed in index tuning. Existing budget-aware index tuning algorithms, however, typically make fast progress early on in terms of the best configuration found but slow down when more and more what-if calls are allocated. This observation of"diminishing return"on index quality leads us to introduce early stopping for budget-aware index tuning, where user specifies a threshold on the tolerable loss of index quality and we stop index tuning if the projected loss with the remaining budget is below the threshold. We further propose Esc, a low-overhead early-stopping checker that realizes this new functionality. Experimental evaluation on top of both industrial benchmarks and real customer workloads demonstrate that Esc can significantly reduce the number of what-if calls made during budget-aware index tuning while incur little or zero improvement loss and little extra computational overhead compared to the overall index tuning time.

Problem

• Reduces time-consuming what-if calls in index tuning
• Addresses diminishing returns in budget-aware index tuning
• Introduces early-stopping to minimize quality loss efficiently

Innovation

• Early-stopping checker for budget-aware tuning
• Reduces what-if calls with minimal quality loss
• Low-overhead solution for index optimization