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Workshop: Machine Learning for Systems
The Unreasonable Effectiveness of LLMs for Query Optimization (Peter Akioyamen, UPenn)
Peter Akioyamen · Zixuan Yi · Ryan Marcus
[
Abstract
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presentation:
Machine Learning for Systems
Sun 15 Dec 8:15 a.m. PST — 4:30 p.m. PST
[
Slides]
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OpenReview]
Sun 15 Dec 2:30 p.m. PST
— 2:40 p.m. PST
Sun 15 Dec 8:15 a.m. PST — 4:30 p.m. PST
Abstract:
Recent work in database query optimization has used complex machine learning strategies, such as customized reinforcement learning schemes. Surprisingly, we show that LLM embeddings of query text contain useful semantic information for query optimization. Specifically, we show that a simple binary classifier deciding between alternative query plans, trained only on a small number of labeled embedded query vectors, can outperform existing heuristic systems. Although we only present some preliminary results, an LLM-powered query optimizer could provide significant benefits, both in terms of performance and simplicity.
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