LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency (2404.12872v1)

Abstract: Query rewrite, which aims to generate more efficient queries by altering a SQL query's structure without changing the query result, has been an important research problem. In order to maintain equivalence between the rewritten query and the original one during rewriting, traditional query rewrite methods always rewrite the queries following certain rewrite rules. However, some problems still remain. Firstly, existing methods of finding the optimal choice or sequence of rewrite rules are still limited and the process always costs a lot of resources. Methods involving discovering new rewrite rules typically require complicated proofs of structural logic or extensive user interactions. Secondly, current query rewrite methods usually rely highly on DBMS cost estimators which are often not accurate. In this paper, we address these problems by proposing a novel method of query rewrite named LLM-R2, adopting a LLM to propose possible rewrite rules for a database rewrite system. To further improve the inference ability of LLM in recommending rewrite rules, we train a contrastive model by curriculum to learn query representations and select effective query demonstrations for the LLM. Experimental results have shown that our method can significantly improve the query execution efficiency and outperform the baseline methods. In addition, our method enjoys high robustness across different datasets.

Enhanced SQL Query Rewriting using LLMs

Introduction to LLM-R² System

The LLM-R² system introduces a transformative approach to SQL query rewriting by integrating LLMs to suggest rewrite rules that can be applied within a database system. Traditional query rewrite systems rely heavily on pre-defined rules, limiting their effectiveness and adaptability. To address these limitations, LLM-R² employs a novel methodology that utilizes the capabilities of LLMs to propose potential rewrite rules, which are then applied using established database platforms. This approach ensures the executability and equivalence of the rewritten queries by relying on validated rewrite rules, while significantly improving query execution efficiency.

System Design and Implementation

General Workflow

The overall architecture of the LLM-R² system is designed to leverage LLMs for enhancing the rule-based query rewrite process. The system processes SQL queries by prompting an LLM with the original query and a set of potential rewrite rules. It then uses the LLM’s suggestions to apply the most effective rules using a regular database rewrite engine.

Demonstration Manager

A central component of the LLM-R² system is the Demonstration Manager. This module optimizes the selection of in-context demonstrations, which are crucial for guiding the LLM in generating useful rewrite rules. The manager functions in two main phases:

• Demonstration Preparation: This stage involves generating a pool of effective rewrite examples using existing methods. It assesses the impact of various rewrite strategies on query performance, ensuring a rich collection of high-quality rewrites for training and application.
• Demonstration Selection: At this stage, a model is trained to select the most appropriate demonstration for any given input query. This selection is crucial as it influences the LLM’s ability to propose effective rewrite rules.

Experimental Evaluation

Setup and Datasets

The LLM-R² system was evaluated using three benchmark datasets: TPC-H, IMDB, and DSB, encompassing a variety of query complexities and data scales. Comparative experiments were conducted against traditional rule-based methods and a baseline LLM-only approach.

Results

The experimental results confirmed that LLM-R² significantly reduces the execution time of SQL queries compared to both the original queries and those rewritten by baseline methods. Notably, the system demonstrated robust performance across all tested datasets, often outperforming traditional methods by a substantial margin.

Theoretical and Practical Implications

The introduction of LLM-R² has several implications for both theory and practice in database query processing:

• Theoretical: LLM-R² challenges conventional rule-based rewrite systems by introducing a model that combines the theoretical underpinnings of LLMs with the practical application of database management systems. This hybrid approach opens new avenues for research into intelligent query optimization.
• Practical: For practitioners, LLM-R² offers a more dynamic and effective tool for query rewriting, capable of adapting to a variety of database schemas and query structures without the need for extensive rule redefinition.

Future Directions

Given the promising results obtained with LLM-R², future research could explore several avenues:

• Model Enhancement: Further refining the model's demonstration selection phase could yield even greater efficiencies in query rewriting.
• LLM Integration: Exploring the integration of other LLM architectures or custom-trained models specifically optimized for SQL contexts could improve both the efficiency and accuracy of rewrites.
• Broadened Application: Extending the LLM-R² approach to other areas of database management, such as automatic indexing or query dispatching, could significantly enhance overall system performance.

Conclusion

The LLM-R² system represents a significant step forward in the field of SQL query rewriting. By effectively integrating LLMs into the rule-based rewrite process, it offers substantial improvements in query execution efficiency while maintaining the high standards of executability and equivalence required in database systems. This innovative approach not only enhances current database management practices but also sets the stage for further developments in intelligent database systems.