This is a page that curates AI-related papers published worldwide. All content here is summarized using Google Gemini and operated on a non-profit basis. Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.
This paper proposes E3-Rewrite, a novel framework leveraging a large-scale language model (LLM) to overcome the limitations of existing rule-based SQL query rewriting methods. Existing methods rely on a fixed set of rules, making it difficult to generalize to new query patterns or complex queries and failing to fully capture effective rewriting strategies. E3-Rewrite constructs context using execution plans and retrieved demos and performs reinforcement learning using a reward function targeting feasibility, equivalence, and efficiency. Through a step-by-step curriculum, it first emphasizes feasibility and equivalence, gradually considering efficiency to ensure stable multi-objective learning.
Takeaways, Limitations
•
Takeaways:
◦
LLM overcomes the limitations of existing rule-based methods and enables more complex and efficient SQL query rewriting.
◦
Generate feasible, equivalent, and efficient queries using contextual construction and reinforcement learning-based reward functions leveraging execution plans and demos.
◦
Achieved up to 25.6% reduction in query execution time and up to 24.4% increase in equivalence-satisfying rewrite results compared to existing methods in various SQL benchmarks.
◦
It is also effective for complex query patterns that existing methods could not effectively optimize.
•
Limitations:
◦
It depends on the performance of LLM, and the limitations of LLM may also affect the performance of E3-Rewrite.
◦
Designing reward functions and optimizing reinforcement learning processes are important and require further research.
◦
There is a possibility of overfitting to certain datasets or query patterns.
◦
Performance can be significantly impacted by the quality of the execution plan and demo data.
